WO2001006446A1 - Automated method for image analysis of residual protein - Google Patents

Abstract

An automated method (Fig. 2) for evaluating the amount of residual protein levels following treatment of cellular specimens (fig. 2, 38). Particular methods include the measurement of maternal neutrophil alkaline phosphatase after treatment with or without urea or heat, measurement of leukocyte acid phosphatase after treatment with or without tartrate, and measurement of leukocyte esterase after treatment with α-naphthol butyrate with or without fluoride.

Description

AUTOMATED METHOD FOR IMAGE ANALYSIS OF RESIDUAL PROTEIN

CLAIM OF PRIORITY

This application claims the benefit of priority from

U.S. Provisional Patent Application No. 60/143,181, filed

July 9, 1999 and is a continuation-in-part of U.S. Patent

Application Serial No. 08/758,436, filed November 27,

1996, which claims the benefit of priority from U.S.

Provisional Patent Application No. 60/026,805, filed

November 30, 1995.

TECHNICAL FIELD

The invention relates generally to light microscopy

and, more particularly, to automated analysis of cellular

specimens containing stained markers.

BACKGROUND OF THE INVENTION

Alkaline phosphatase concentrations usually increase

in blood neutrophils of normal pregnant women. However,

the maternal neutrophil alkaline phosphatase (NAP) in

Down's syndrome pregnancies (women with trisomy 21

fetuses) differs from NAP found in normal pregnancies.

NAP from women with Down's syndrome pregnancies is characterized by: (1) an increase in NAP enzyme activity

over that found in normal pregnancies, (2) NAP thermal

stability, (2) NAP stability in urea, (3) a significant

decrease in reactivity with anti-liver-type alkaline

phosphatase (AP) ; (4) low reactivity with anti-placental-

type AP or anti-intestinal-type AP antibodies; (5)

altered response to AP enzyme inhibitors; and (6) marked

dispersion of NAP lead citrate reaction products or

anti -NAP antibody colloidal gold-labeling in neutrophil

cytoplasm, as detected by electron microscopy. These

characteristics suggest that neutrophils of a woman with

a trisomy 21 fetus contain two AP isoenzymes: the

liver/bone type AP and an atypical AP that is related to

the early placental form. Thus, the non-specific

alkaline phosphatase isoenzy e can be an "enzyme marker"

to diagnose Down's syndrome pregnancies.

The histochemical measurement of maternal NAP has a

high detection rate for the prenatal detection of Down's

syndrome pregnancies. However, because the histochemical

method is laborious and subjective to use, the method has

not gained widespread acceptance in prenatal screening

programs . Some automated methods have been developed. Tafas

et al . have developed an image analysis method for the

measurement of neutrophil alkaline phosphatase (NAP) and

established a correlation of urea-resistant fraction of

NAP (URNAP) /NAP scoring between the manual and automated

methods for prenatal screening of Down's syndrome (U.S. Patent No. 5,352,613 to Tafas et al . ; Tafas et al . , Fetal

Diagn . Ther. 11 (4) .-254-259, 1996). Measurements

("scores") obtained by manual and automated methods

correlate, but the automated scoring is threefold faster.

However, a less laborious and subjective automated image

analysis method could have benefits in the medical arts.

SUMMARY OF THE INVENTION

The invention provides an automated method for the

measurement of a residual component of a cellular protein. A specimen (sample) to be stained with a

cytochemical stain is obtained from a subject. At least

one subsample is treated before being stained, such that

the treatment may affect the measurable protein level. At

least one subsample is not treated, to serve as a control . The untreated subsample and the treated subsample are stained together, for uniformity of

staining. An apparatus automatically selects a position

in each subsample for candidate objects of interest and

obtains a low magnification color digital image of the

candidate objects of interest. The apparatus

automatically filters the pixels of the candidate object of interest with a low pass filter and morphologically

processes the candidate object of interest pixels to

identify artifact pixels, identifying the candidate objects of interest from the remaining candidate object

of interest pixels in the subsample not identified as artifact pixels. The apparatus is adjusted to high

magnification for automatically acquiring a high magnification image of the subsample, at the location coordinates corresponding to the low magnification image,

for each candidate object of interest. The apparatus

automatically transforms pixels of the high magnification

image in the first color space to a second color space to differentiate high magnification candidate objects of interest pixels from background pixels, identifying

objects of interest from the candidate object of interest

pixels in the second color space. The protein level is

scored in the untreated and the treated subsamples. The

value Delta (Δ) is determined (Δ = [protein level in the treated subsamples] / [protein level in the untreated

subsamples] ) as a measurement of the residual component

of the cellular protein.

In particular embodiments, the method is for the

measurement of maternal neutrophil alkaline phosphatase after treatment with or without urea or heat (diagnostic for Down's syndrome pregnancies), measurement of

leukocyte acid phosphatase after treatment with or

without tartrate (diagnostic for hairy cell leukemia) ,

and measurement of leukocyte esterase after treatment with -naphthol butyrate with or without fluoride

(diagnostic for leukemia) .

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of an apparatus for

automated cell image analysis.

FIG. 2 is a block diagram of the apparatus shown in

FIG. 1.

FIG. 3 is a block diagram of the microscope

controller of FIG. 2.

FIG. 4 is a plan view of the apparatus of FIG. 1

having the housing removed.

FIG. 5 is a side view of a microscope subsystem of

the apparatus of FIG. 1.

FIG. 6 shows a slide carrier. FIG. 6a is a top view

of a slide carrier for use levity the apparatus of FIG.

1. FIG. 6b is a bottom view of the slide carrier of FIG.

6a.

FIG. 7 shows views of an automated slide handling

subsystem. FIG. 7a is a top view of an automated slide

handling subsystem of the apparatus of FIG. 1. FIG. 7b is a partial cross-sectional view of the automated slide handling subsystem of FIG. 7a taken on line A-A.

FIG. 8 shows end views of the input module of the

automated slide handling subsystem. FIG. 8a-8d

illustrate the input operation of the automatic slide handling subsystem.

FIG. 9a-9d illustrate the output operation of the

automated slide handling subsystem.

FIG. 10 is a flow diagram of the procedure for

automatically determining a scan area.

FIG. 11 shows the scan path on a prepared slide in

the procedure of FIG. 10.

FIG. 12 illustrates an image of a field acquired in

the procedure of FIG. 10. FIG. 13 shows flow diagrams of procedures for determining a focal position. FIG. 13a is a flow diagram

of a generally preferred procedure for determining a focal position. FIG. 13b is a flow diagram of a

preferred procedure for determining a focal position for

neutrophils stained with Fast Red and counter-stained

with hematoxylin. FIG. 14 is a flow diagram of a procedure for automatically determining initial focus.

FIG. 15 shows an array of slide positions for use in

the procedure of FIG. 14.

FIG. 16 is a flow diagram of a procedure for automatic focusing at a high magnification.

FIG. 17 shows flow diagrams for processes to locate

and identify objects of interest in a stained cellular

specimen on a slide. FIG. 17a is a flow diagram of an

overview of the preferred process to locate and identify

objects of interest in a stained cellular specimen on a slide. FIG. 17b is a flow diagram of a procedure for

color space conversion.

FIG. 18 is a flow diagram of a procedure for

background suppression by dynamic thresholding.

FIG. 19 is a flow diagram of a procedure for

morphological processing.

FIG. 20 is a flow diagram of a procedure for blob analysis .

FIG. 21 is a flow diagram of a procedure for image

processing at a high magnification. FIG. 22 illustrates a mosaic of cell images produced

by the apparatus .

FIG. 23 is a flow diagram of a procedure for

estimating the number of nucleated cells in a scan area.

FIG. 24 illustrates the apparatus functions

available in a user interface of the apparatus.

DETAILED DESCRIPTION

Principle

The invention provides an automated method cell

image analysis which determines residual protein levels,

in a greatly improved manner over manual scoring

techniques. The invention provides a method ("the Delta

method") of quantitating the residual protein remaining

in a cell after a treatment of cells. The term "residual

protein levels" refers to the measurable protein of

interest remaining in a cell after the treatment; thus

the term "residual protein" can refer either the actual

protein remaining in the cell or the remaining protein

activity, for example, the remaining enzyme activity.

While current automated methods can eliminate the problem

of laboratory-to-laboratory and technologist-to- technologist scoring variations through an objective

imaging processing scoring technique, the problems caused

by staining variations and individual patient "nominal"

measurement ("score") variations cannot be eradicated by

automation. However, by combining the scientific

advantages of automation and the Delta method, as well as

the greatly increased speed with which residual protein

scores can be evaluated, the invention is a major

improvement over methods currently available. The Delta

method, as automated, has considerable diagnostic

potential .

Continued dependence on manual input can lead to

errors in image analysis. Additionally, manual methods

can be extremely time consuming and can require a high

degree of training to properly identify or quantify

cells. The associated manual labor leads to a high cost

for these procedures. A need exists, therefore, for an

improved automated cell image analysis system which can

quickly and accurately scan large amounts of biological

material on a slide.

Unless otherwise defined, all technical and

scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art

to which this invention belongs. Although methods and

materials similar or equivalent to those described herein

can be used in the practice or testing of the present

invention, suitable methods and materials are described

below.

All publications, patent applications, patents, and

other references mentioned herein are incorporated by

reference in their entirety. In case of conflict, the

present application, including definitions, will control.

In addition, the materials and methods described herein

are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be

apparent from the following detailed description, the

drawings, and from the claims.

Del ta Method

The Delta method is the measurement of a residual

amount of cellular enzyme or protein following treatment

of a cell. Various cellular treatments include acid,

base, high salt, low salt, heat, an enzyme inhibitor

(such as levamisole) or a detergent. A cellular specimen (a "sample") is split to provide two or more subsamples.

In at least one subsample, the total protein is measured,

or scored. In at least one other subsample, the residual

protein levels present after treatment is scored. The

residual protein is compared to the total protein score

from a subsample obtained from the same specimen.

The terms "sample" and "subsample" include cellular

material derived from a subject. Such samples include

but are not limited to hair, skin samples, tissue sample,

cultured cells, cultured cell media, and biological

fluids. A tissue is a mass of connected cells { e . g. , CNS

tissue, neural tissue, eye tissue, placental tissue)

derived from a human or other animal and includes the

connecting material and the liquid material in

association with the cells. A biological fluid is a

liquid material derived from a human or other animal.

Such biological fluids include but are not limited to

blood, plasma, serum, serum derivatives, bile, phlegm,

saliva, sweat, amniotic fluid, and cerebrospinal fluid

(CSF) , such as lumbar or ventricular CSF. The term

"sample" also includes media containing isolated cells.

The quantity of sample required to obtain a reaction may be determined by one skilled in the art by standard

laboratory techniques. The optimal quantity of sample

may be determined by serial dilution.

Current automated techniques do not allow for

variations in either a patient's protein score that is

unrelated to the condition being diagnosed or in a method

used to calculate the protein score. Regarding variations

in methods used for calculating protein scores, the

absolute value of a patient's protein score depends

crucially on the quality of specimen staining and the

standards applied in scoring the individual specimen

(which typically is subjective, varying from laboratory-

to-laboratory and technologist-to-technologist) . Hence,

the score for a given slide, as compared to an absolute

standard (as is currently typical in laboratory

procedures) varies depending upon how that slide is

stained and scored. This variation can be on the order

of the difference expected between Down's pregnancies and

normal pregnancies, meaning that an incorrect patient

diagnosis becomes highly likely.

The Delta method compensates for the sources of

variation by using the patient herself as a control, rather than using the statistical distributions derived

from other patients as the control. This compensation is accomplished by preparing multiple subsample slides from

the same specimen and then exposing subsamples to various treatments. An untreated subsample slide serves as the

control. All of the subsample slides are then stained

together as a batch and scored to determine the

difference that the treatment causes in the protein scores for that particular patient.

The quantity "Delta" can be written as:

Δ = [protein level in the treated subsamples] / [protein

level in the untreated subsamples]

where the total protein level is determined from the untreated subsamples and the residual protein level is determined from the treated subsamples. A Δ value can

indicate the presence of a condition of interest. By

analyzing many cases, the ranges of Δ's indicative of a

particular condition can be determined.

The advantages of the Delta method are several .

First, the problem with variations in the individual

patient and her "nominal" score is avoided because each

patient is being judged against her own personal standard. The characteristics of this phenomenon are

reflected in the statistical determination of what the

different Δ ranges represent.

A second advantage of the Delta method is that the

variability of scoring methods caused by inter-laboratory

and inter-technologist biases is removed because the same

scoring standard is applied to the test slides as to the

control . The method preferably includes that the scoring

of all slides from a patient be scored by the same

technologist. Also, by staining the test slides in the

same batch as the control, staining variations can be

removed because any batch-specific or reagent lot-

specific variation affects all the slides. In current

standard methods, such variations result in an overall

shift in the protein score of the test slides, which are

then be compared to statistical results to result in an

incorrect conclusion.

One problem with current automated systems is the

difficulty in evaluating the amount of a marker present

in a cellular specimen. (A "marker" may be any

polypeptide sequence or feature of the protein that can

be detected.) For example, proteins is stained to cause an insoluble product which identifies the marker to

become colored, often red. A subsample can be counter-

stained, for example with hematoxylin, to make the nuclei

of the cells become blue in color. A pathologist viewing

such a slide detects cells of interest by locating those

cells having a nucleus of the color, shape, and size

expected for a cell of interest. The pathologist

subjectively evaluates the intensity of the red color for

each located cell and assigns a score to each. The

pathologist then subjectively determines whether the

number of intensely red cells and moderately red cells

are sufficient to determine a particular condition.

According to that procedure, a pathologist sums the

subjectively assigned ratings for marker identifying

precipitate in the first 100 neutrophils to arrive at a

score which may be used to determine the relative red

intensity of the neutrophils in the cellular sample.

This score is then used to determine whether the

condition associated with the presence of the marker is

indicated.

In one embodiment, the Delta method measures

residual enzyme. In particular, the NAP Delta method measures maternal neutrophil alkaline phosphatase, for evaluating the likelihood that a pregnant woman is

carrying a Down's syndrome fetus. Resistance to urea or

heat exposure of NAP in the peripheral blood of a

pregnant woman correlates with an increased risk of

Down's syndrome incidence in the fetus. The resistance

to urea or heat exposure is measured by comparing the NAP

scores for urea-exposed or heat-exposed blood subsamples

to NAP scores for untreated blood subsamples. Current automated methods for measuring NAP do not

allow for variations in the patient's particular NAP

score, unrelated to the pregnancy. Pregnancy is known to

lead to a significant increase in NAP score. But this

NAP score is an increase from the woman's "nominal" NAP

score before the pregnancy, so that a woman with a higher nominal NAP score should have a greater score when

elevated due to pregnancy. The variation in nominal

scores is, in the art, taken into account only in the mean, for a large number of patients, rather than for

each individual patient.

The Delta method uses the patient herself as a

control, not the statistical distributions. Multiple slides (subsamples) are prepared from the same specimen

(sample) . One slide can be exposed to urea, one slide can

be exposed to heat, and one slide untreated. The

untreated slide serves as the control. All of the slides

are stained together as a batch. When the slides are

scored, the difference that urea or heat exposure causes

in the NAP scores for that patient can be examined.

Alternatively, enzyme inhibitors of NAP can be used as

the treatment. For example, alkaline phosphatase

inhbitors such as levamisole or tetramisole may be used

in the treated samples.

According to the art, inhibitors of NAP, such as

urea, heat, and/or levamisole exposure of the subsamples

should not affect the NAP scores for a patient carrying a

Down's syndrome fetus as much as for a patient carrying a

normal fetus. Thus, for a Down's syndrome pregnancy, the

percentage decrease in NAP score from the untreated,

control slide to the exposed or treated slides should be

less than for a normal patient . This can be written in

terms of the quantities "Delta"as: Δ_u = [urea exposed NAP score] / [control NAP score]

and

Δ_h = [heat exposed NAP score] / [control NAP score] .

A Down's syndrome pregnancy should have a Δ_u and Δ_h

greater than a normal pregnancy. By analyzing many

cases, the ranges of Δ_u and Δ_h can be determined, where

the ranges define the high statistical likelihood of

Down's syndrome pregnancy as compared with a normal

pregnancy .

In the Delta method, the overall phenomenon of urea

and heat resistance in Down's syndrome pregnancies is

used only after this component of patient-to-patient

variation has been removed. The characteristics of this

phenomenon are reflected in the statistical determination

of what the different ranges of Δ_u and Δ_h represent.

A blood smear used to evaluate the presence of

alkaline phosphatase in neutrophils can be stained with a

solution of naphthol AS-bisphosphate salt and fast red

violet LB. AP coverts the soluble colorless substrate to

a colored insoluble precipitate. The presence of NAP in

the subsample is indicated by the red color of a precipitate formed by enzymatic hydrolysis. Many other

AP stains are known to those of skill in the art.

The subsample is usually counter-stained, for

example, with hematoxylin, to produce a blue insoluble precipitate in white blood cell nuclei. The

stained/counter-stained white blood cells can be

identified by having blue nuclei. Neutrophil cells are

identified based on the shape and size of the nuclei.

Thus, the low magnification processing identifies

candidate objects of interest, such as neutrophils, from the color, shape, and size of objects in the image.

Current automated systems have difficulty in

evaluating the amount of NAP present on a slide. For

NAP, the pathologist usually grades the neutrophils with a rating of 0 , 1, 2, 3, or 4 in accordance with a grading

scale, such as the one provided with the procedure for

using the reagent kit sold by Sigma Diagnostics for

demonstrating alkaline phosphatase activity in

leukocytes. According to that procedure, a pathologist

sums the subjectively assigned ratings for marker identifying precipitate in the first 100 neutrophils to

arrive at a score which may be used to determine the relative red intensity of the neutrophils in the cellular

sample. This score may then be used to determine whether

the condition associated with the presence of the marker

is indicated.

The Delta method can also be used to measure acid

phosphatase isoenzyme 5 (tartrate-resistant) . The

demonstration of tartrate-resistant acid phosphatase

(TRAP) activity has long been a cornerstone in the

diagnosis of hairy cell leukemia (HCL) . Acid phosphatase

exists as five isoenzymes. While acid phosphatase is

present in almost all leukocytes, isoenzyme 5 is

restricted to the cells of hairy cell leukemia, as well

as some other non-hematopoietic tissues. Isoenzyme 5 is

distinguished form other isoenzymes by an inability to be

inhibited by tartaric acid. The neoplastic cells in hairy

cell leukemia stain strongly positive for acid

phosphatase in the presence of tartrate.

The specimen for a TRAP assay can be unstained

peripheral blood or bone marrow aspirate smears,

anticoagulated peripheral blood or bone marrow, or other

body fluids In one method for performing a TRAP assay,

subsamples are prepared on slides. Stock solutions are

prepared, including (A) pararosanilin-HCl stock (pararosanilin 1 g; distilled water 20 ml; concentrated

HCl 5 ml. The pararosanilin is dissolved in the distilled

water and hydrochloric acid is added. The resulting

solution is heated gently, cooled, filtered, and stored

in aliquots in a refrigerator.); (B) sodium nitrite stock

(sodium nitrite 2 g; distilled water 50 ml. This solution

is prepared fresh or made into 0.4 ml aliquots and stored

in a deep freeze.); (c) veronal-acetate buffer stock

(sodium acetate (3 H₂0) 3.88 g; sodium barbitone 5.88 g; distilled water 200 ml) ; and (D) naphthol ASB1 phosphate stock (naphthol ASB1 phosphate 50 mg; dimethyl formamide

5 ml . This solution is put into 0.5 ml aliquots and

stored in a deep freeze.) .

Preparation of incubating solution includes the

following: First, pararosanilin-HCl (A) 0.4 ml and sodium

nitrite (40 mg/ml) (B) 0.4 ml. Pararosanilin is added

drop by drop to the thawed sodium nitrite, shaking well

after each addition until the solution is corn-colored

(leave to stand for at least 30 seconds) . Then, naphthol ASB1 phosphate (D) 0.5 ml , veronal acetate buffer stock

(c) 2.5 ml, and distilled water 6.5 ml are mixed

together well and then added the pararosanilin/sodium

nitrite solution. The pH is adjusted to 4.7-5.0, the

solution filtered, and used immediately. For

demonstrating tartrate resistant acid phosphatase, add 28

mg/10 ml of sodium tartrate to the incubating solution. A

control batch without sodium tartrate is used for control

sections .

The method of staining is as follows: (1) incubate

subsamples for 10-60 sec; (2) wash well in distilled and

then tap water (as the acidity of the solution can make

hematoxylin staining muddy) ; (3) counterstain, with 2%

methyl green for 15-30 sec. (hematoxylin can be used as

an alternative) ; (4) wash; and (5) dehydrate, then clear.

The results are that acid phosphatase activity stains

red.

If at any point the incubating solution is bright

red the method must be repeated as this means that the

sodium nitrite and pararosanilin have not reacted

adequately. The incubating medium should be a slightly

opalescent straw color. The Delta method can also be used to measure

α-naphthyl butyrate esterase. α-naphthyl butyrate

esterase is a non-specific esterase (NSE) with an

activity that is inhibited by fluoride. The presence of

α-naphthyl butyrate esterase is a measure of neutrophil

functional maturity, since α-naphthyl butyrate esterase

is present in high er levels in lymophoblasts than in

mature neutrophils. In other words, an increased

α-naphthyl butyrate esterase level can indicate leukemia.

This cytochemical stain is commonly used for the

diagnosis of acute myelogenous leukemias with monocytic

differentiation (FAB types M4 and M5) and is part of a

routine cytochemical stain panel for the diagnosis of

acute leukemia.

In the method of the invention, α-naphthyl butyrate

esterase is measured by the Delta method using a

non-specific esterase (NSE) assay with α-naphthyl butyrate

as a substrate and treatment sodium fluoride. An NSE

assay with α-naphthyl butyrate as a substrate no

treatment sodium fluoride serves as the control . The

specimen can be unstained peripheral blood or bone marrow

aspirate smears, anticoagulated peripheral blood or bone marrow, or other body fluids.

In another embodiment, the method measures residual

protein immunologically. The method of the invention may

use antibodies that are immunoreactive or bind to

epitopes of the target proteins. The term "epitope"

refers to any antigenic determinant on an antigen to

which the paratope of an antibody binds. Epitopes usually

consist of chemically active surface groupings of

molecules such as amino acids or sugar side chains and

usually have specific three dimensional structural

characteristics, as well as specific charge

characteristics. The term "antibody" includes intact

molecules as well as fragments thereof, such as Fab,

Fab', F(ab')₂, Fv, and single chain- antibody that can bind

the epitope. These antibody fragments retain some

ability to bind selectively with antigen or receptor.

The antibody may consist essentially of pooled monoclonal

antibodies with different epitopic specificities, as well

as distinct monoclonal antibody preparations. Monoclonal

antibodies are made from antigen containing fragments of

the protein by methods well known in the art (see,

Kohler, et al . , Nature 256 : 495, 1975; Current Protocols in Molecular Biology, Ausubel et al . , ed. , 1989) . Methods

of making these fragments are known in the art (see, for

example, Harlow & Lane, Antibodies: A Laboratory Manual ,

Cold Spring Harbor Laboratory, New York, 1997) . If

needed, polyclonal or monoclonal antibodies can be

further purified, for example, by binding to and elution

from a matrix to which the peptide or a peptide to which

the antibodies are raised is bound. Those of skill in

the art will know of various techniques common in the

immunology arts for purification and/or concentration of

polyclonal antibodies, as well as monoclonal antibodies

( see, e . g. , Colligan et al . , Unit 9, Current Protocols in

Immunology, Wiley Interscience, 1997) . Antibodies,

including polyclonal and monoclonal antibodies, chimeric

antibodies, single chain antibodies and the like, have

with the ability to bind with high immunospecificity to

the target proteins. Antibodies may be employed in known

immunological procedures for qualitative or quantitative

detection of these proteins in cells, tissue samples,

sample preparations or fluids. These antibodies can be

unlabeled or suitably labeled with chromogenic dyes,

fluorogenic dyes, or coupled to enzymes that catalyze chromogenic or fluorogenic reactions. Thus, the binding

of antibody to target protein is assayable by image

analysis .

ELISA or other immunoaffinity methods can be used to

quantify protein. ELISA refers to an enzyme-_.linked

jLmmunosorbant assay method for detecting antigens or

antibodies using enzyme-substrate reactions. The enzymes

are generally coupled to antibodies (either to antibodies

specific for the antigen or to anti-immunoglobulin) . The

amount of enzyme conjugate determined from the turnover

of an appropriate substrate, preferably one that produces

a colored product .

The Delta Method may be further performed with an

automated system. Such an automated system will quantify

the various samples and subsamples and calculate a Delta.

The automated method will be further understood with

reference to the description below.

Automated System

The invention provides a method for automated cell

image analysis that eliminates the need for operator

input to locate cell objects for analysis. A slide prepared with a subsample and reagent is

placed in a slide carrier which preferably holds four

slides. The slide carriers are loaded into an input

hopper. The operator enters data identifying the size,

shape and location of a scan area on each slide, or,

preferably, the system automatically locates a scan area

for each slide during slide processing. The operator

then activates the system for slide processing. At

system activation, a slide carrier is positioned on an X-

Y stage of an optical system. Any bar codes used to

identify slides are then read and stored for each slide

in a carrier. The entire slide is rapidly scanned at a

low magnification, typically lOx. At each location of

the scan, a low magnification image is acquired and

processed to detect candidate objects of interest.

Preferably, color, size and shape are used to identify

objects of interest. The location of each candidate

object of interest is stored.

At the completion of the low level scan for each

slide, in the carrier on the stage, the optical system is

adjusted to a high magnification such as 40x or 60x, and

the X-Y stage is positioned to the stored locations for the candidate objects of interest on each slide in the

carrier. A high magnification image is acquired for each

candidate object of interest and a series of image

processing steps are performed to confirm the analysis

which was performed at low magnification. A high

magnification image is stored for each continued object

or interest. These images are then available for

retrieval by a pathologist or cytotechnologist to review

for final diagnostic evaluation. Having stored the

location of each object of interest, a mosaic comprised

of the candidate objects of interest for a slide may be

generated and stored. The pathologist or

cytotechnologist may view the mosaic or may also directly

view the slide at the location of an object of interest

in the mosaic for further evaluation. The mosaic may be

stored on magnetic media for future reference or may be

transmitted to a remote site for review or storage. The

entire process involved in examining a single slide takes

on the order of 9-15 minutes (min) depending on scan area

size and the number of detected candidate objects of

interest . The processing of images acquired in the automated

scanning of the present invention preferably includes the

steps of transforming the image to a different color

space; filtering the transformed image with a low pass

filter; dynamically thresholding the pixels of the

filtered image to suppress background material;

performing a morphological function to remove artifacts

from the thresholded image; analyzing the thresholded

image to determine the presence of one or more regions of

connected pixels having the same color; and categorizing

every region having a size greater than a minimum size as

a candidate object of interest.

In another aspect of the invention, the scan area is

automatically determined by scanning the slide; acquiring

an image at each slide position; analyzing texture

information or each image to detect the edges of the

subsample; and storing the locations corresponding to the

detected edges to define the scan area.

According to yet another aspect of the invention,

automated focusing of the optical system is achieved by

initially determining a focal plane from an array of

points or locations in the scan area. The derived focal plane enables subsequent rapid automatic focusing in the

low power scanning operation. The focal plane is

determined by determining proper focal positions across

an array of locations and performing an analysis such as

a least squares fit of the array of focal positions to

yield a focal plane across the array. Preferably, a

focal position at each location is determined by

incrementing the position of a Z stage for a fixed number

of coarse and fine iterations. At each iteration, an

image is acquired and a pixel variance or other optical

parameter about a pixel mean for the acquired image is

calculated to form a set of variance data. A least

squares fit is performed on the variance data according

to a known function. The peak value of the least squares

fit curve is selected as an estimate of the best focal

position.

In another aspect of the present invention, another

focal position method for high magnification locates a

region of interest centered about a candidate object of

interest within a slide which were located during an

analysis of the low magnification images. The region of

interest is preferably n columns wide, where n is a power of 2. The pixels of this region are then processed using

a Fast Fourier Transform to generate a spectra of

component frequencies and corresponding complex magnitude

for each frequency component. Preferably, the complex

magnitude of the frequency components which range from

25% to 75% of the maximum frequency component are squared

and summed to obtain the total power for the region of

interest. This process is repeated for other Z positions

and the Z position corresponding to the maximum total

power for the region of interest is selected as the best

focal position. This process is preferably used to

select a Z position for regions of interest for slides

containing neutrophils stained with Fast Red to identify

alkaline phosphataase in cell cytoplasm and counter-

stained with hematoxylin to identify the nucleus of the

neutrophil cell. This focal method may be used with

other stains and types of cellular specimens.

In still another aspect of the invention, a method

for automated slide handling is provided. A slide is

mounted onto a slide carrier with a number of other

slides side-by-side. The slide carrier is positioned in

an input feeder with other slide carriers to facilitate automatic analysis of a batch of slides. The slide

carrier is loaded onto the X-Y stage of the optical

system for the analysis of the slides thereon.

Subsequently, the first slide carrier is unloaded into an

output feeder after automatic image analysis and the next carrier is automatically loaded.

Referring to the FIGURES, an apparatus for automated

cell image analysis of cellular specimens is generally

indicated by reference numeral 10 as shown in perspective view in FIG. 1 and in block diagram form in FIG. 2. The

apparatus 10 comprises a microscope subsystem 32 housed in a housing 12. The housing 12 includes a slide carrier

input hopper 16 and a slide carrier output hopper 18. A

door 14 in the housing 12 secures the microscope

subsystem from the external environment . A computer

subsystem comprises a computer 22 having a system processor 23, an image processor 25 and a communications

modem 29. The computer subsystem further includes a

computer monitor 26 and an image monitor 27 and other external peripherals including storage device 21, track

ball device 30, keyboard 28 and color printer 35. An

external power supply 24 is also shown for powering the system. Viewing oculars 20 of the microscope subsystem

project from the housing 12 for operator viewing. The

apparatus 10 further includes a CCD camera 42 for

acquiring images through the microscope subsystem 32. A

microscope controller 31 under the control of system

processor 23 controls a number of microscope subsystem

functions described further in detail. An automatic

slide feed mechanism 37 in conjunction with X-Y stage 38

provide automatic slide handling in the apparatus 10. An

illumination light source 48 projects light onto the X-Y

stage 38 which is subsequently imaged through the

microscope subsystem 32 and acquired through CCD camera

42 for processing in the image processor 25. A Z stage

or focus stage 46 under control of the microscope

controller 31 provides displacement of the microscope

subsystem in the Z plane for focusing. The microscope

subsystem 32 further includes a motorized objective

turret 44 for selection of objectives.

The purpose of the apparatus 10 is for the

unattended automatic scanning of prepared microscope

slides for the detection and counting of candidate

objects of interest such as cells. The preferred embodiment may be utilized for rare event detection in

which there may be only one candidate object of interest

per several hundred thousand normal cells, e . g. , one to

five candidate objects of interest per 2 square

centimeter area of the slide. The apparatus 10

automatically locates and counts candidate objects of

interest and estimates normal cells present in a cellular

specimen on the basis of color, size and shape

characteristics. A number of stains are used to

preferentially stain candidate objects of interest and

normal cells different colors, so that such cells can be

distinguished from each other.

A subsample can be prepared with a reagent to obtain a

colored insoluble precipitate. The apparatus is used to

detect this precipitate as a candidate object of

interest .

During operation of the apparatus 10, a pathologist

or laboratory technician mounts prepared slides onto

slide carriers. A slide carrier 60 is illustrated in

FIG. 8 and are described further below. Each slide

carrier holds up to 4 slides. Up to 25 slide carriers

are then loaded into input hopper 16. The operator can specify the size, shape and location of the area to be

scanned or alternatively, the system can automatically

locate this area. The operator then commands the system

to begin automated scanning of the slides through a

graphical user interface. Unattended scanning begins

with the automatic loading of the first carrier and slide

onto the precision motorized X-Y stage 38. A bar code

label affixed to the slide is read by a bar code reader

33 during this loading operation. Each slide is then

scanned at a user selected low microscope magnification,

for example, lOx, to identify candidate cells based on

their color, size and shape characteristics. The X-Y

locations of candidate cells are stored until scanning is

completed.

After the low magnification scanning is completed,

the apparatus automatically returns to each candidate

cell, reimages and refocuses at a higher magnification

such as 4Ox and performs farther analysis to confirm the

cell candidate. The apparatus stores an image of the

cell for later review by a pathologist. All results and

images can be stored to a storage device 21 such as a

removable hard drive or DAT tape or transmitted to a remote site for review or storage. The stored images for

each slide can be viewed in a mosaic of images for

further review. In addition, the pathologist or operator

can also directly view a detected cell through the

microscope using the included oculars 20 or on image

monitor 27.

Having described the overall operation of the

apparatus 10 from a high level, the further details of

the apparatus are now be described. Referring to FIG.

3, the microscope controller 31 is shown in more detail.

The microscope controller 31 includes a number of

subsystems connected through a system bus . A system

processor 102 controls these subsystems and is controlled

by the apparatus system processor 23 through an RS 232

controller 110. The system processor 102 controls a set

of motor-control subsystems 114 through 124 which control

the input and output feeder, the motorized turret 44, the

X-Y stage 38, and the Z stage 46 (FIG. 9) . The histogram

processor 108 receives input from CCD camera 42 for

computing variance data during the focusing operation

described further herein. The system processor 102 further controls an

illumination controller 106 for control of substage

illumination 48. The light output from the halogen light

bulb which supplies illumination for the system can vary

over time due to bulb aging, changes in optical

alignment, and other factors. In addition, slides which

have been "over stained" can reduce the camera exposure

to an unacceptable level . To compensate for these

effects, the illumination controller 106 is included.

This controller is used in conjunction with light control

software to compensate for the variations in light level.

The light control software samples the output from the

camera at intervals (such as between loading of slide

carriers) , and commands the controller to adjust the

light level to the desired levels. In this way, light

control is automatic and transparent to the user and adds

no additional time to system operation.

The system processor 23 is preferably an IBM

compatible PC with an Intel Pentium 90 MHZ processor, 32

MB of RAM, and two IGB hard drives with one hard drive

being removable. The operating system for system

processor 23 is Windows for Workgroups 3.1 available from Microsoft Corporation (Redmond, WA) . Image processor 25

is preferably a Matrox Imaging Series 640 board set

available from Matrox Electronics Systems, Ltd. (Dorval,

Quebec, CA) . The preferred image processor is provided

with support software and the Matrox Imaging Library

(MIL) . Microscope controller system processor 102 is an

Advanced Micro Devices AMD29K device.

Referring now to FIG. 4 & 5, further detail of the

apparatus 10 is shown. FIG. 4 shows a plan view of the

apparatus 10 with the housing 12 removed. A portion of

the automatic slide feed mechanism 37 is shown to the

left of the microscope subsystem 32 and includes slide

carrier unloading assembly 34 and unloading platform 36

which in conjunction with slide carrier unloading hopper

18 function to receive slide carriers which have been

analyzed.

Vibration isolation mounts 40, shown in further

detail in FIG. 5, are provided to isolate the microscope

subsystem 32 from mechanical shock and vibration that can

occur in a Apical laboratory environment . In addition to

external sources of vibration, the high speed operation

of the X-Y stage 38 can induce vibration into the microscope subsystem 2. Such sources of vibration can be

isolated from the electro-optical subsystems to avoid any

undesirable effects on image quality. The isolation

mounts 40 comprise a spring 40a and piston 40b submerged

in a high viscosity silicon gel which is enclosed in an elastomer membrane bonded to a casing to achieve damping factors on the order of 17% to 20%.

The automatic slide handling feature of the present

invention is now described. The automated slide handling subsystem operates on a single slide carrier at a time.

A slide carrier 60 is shown in FIG. 6a & 6b which provide

a top view and a bottom view, respectively. The slide

carrier 60 includes up to four slides 70 mounted with

adhesive tape 62. The carrier 60 includes ears 64 for hanging the carrier in the output hopper 18. An undercut 66 and pitch rack 68 are formed at the top edge of the slide carrier 60 for mechanical handling of the slide

carrier. A keyway cutout 65 is formed in one side of the

carrier 60 to facilitate carrier alignment. A prepared slide 72 mounted on the slide carrier 60 includes a

sample area 72a and a bar code label area 72b. FIG. 7a provides a top view of the slide handling

subsystem which comprises a slide input module 15, a

slide output module 17 and X-Y stage drive belt 50. FIG.

7b provides a partial cross-sectional view taken along

line A-A of FIG. 7a.

The slide input module 15 includes a slide carrier

input hopper 16, loading platform 52 and slide carrier

loading subassembly 54. The input hopper 16 receives a

series of slide carriers 60 (FIG. 6a & 6b) in a stack on

loading platform 52. A guide key 57 protrudes from a

side of the input hopper 16 to which the keyway cutout 65

(FIG. 6a) of the carrier is fit to achieve proper

alignment .

The input module 15 further includes a revolving

indexing cam 56 and a switch 90 mounted in the loading

platform 52, the operation of which is described further

below. The carrier subassembly 54 comprises an infeed

drive belt 59 driven by a motor 86. The infeed drive

belt 59 includes a pusher tab 58 for pushing the slide

carrier horizontally toward the X-Y stage 38 when the

belt is driven. A homing switch 95 senses the pusher tab

58 during a revolution of the belt 59. Referring specifically to FIG. 7a, the X-Y stage 38 is shown with x position and y postion motors 96 and 97

respectively which are controlled by the microscope

controller 31 (FIG. 3) and are not considered part of the

slide handling subsystem. The X-Y stage 38 further

includes an aperture 55 for allowing illumination to

reach the slide carrier. A switch 91 is mounted adjacent

the aperture 55 for sensing contact with the carrier and

thereupon activating a motor 87 to drive stage drive belt

50 (FIG. 7b) . The drive belt 50 is a double sided timing belt having teeth for engaging pitch rack 68 of the

carrier 60 (FIG. 6b) .

The slide output module 17 includes slide carrier

output hopper 18, unloading platform 6, and slide carrier unloading subassembly 34. The unloading subassembly 34

comprises a motor 89 for rotating the unloading platform

36 about shaft 98 during an unloading operation described

further below. An outfeed gear 93 driven by motor 88

rotatably engages the pitch rack 68 of the carrier 60 (FIG. 6b) to transport the carrier to a rest position

against switch 92. A springloaded hold-down mechanism

holds the carrier in place on the unloading platform 36. The slide handling operation is now described.

Referring to FIG. 8, a series of slide carriers 60 are

shown stacked in input hopper 16 with the top edges 60a

aligned. As the slide handling operation begins, the indexing cam 56 driven by motor 85 advances one

revolution to allow only one slide carrier to drop to the

bottom of the hopper 16 and onto the loading platform 52.

FIG. 8a-8d show the cam action in more detail. The

indexing cam 56 includes a hub 56a to which are mounted upper and lower leaves 56b and 56c respectively. The

leaves 56b, 56c are semicircular projections oppositely

positioned and spaced apart vertically. In a first

position shown in FIG. 8a, the upper leaf 56b supports the bottom carrier at the undercut portion 66. At a

position of the indexing cam 56 rotated 180°, shown in

FIG. 8b, the upper leaf 56b no longer supports the

carrier and instead the carrier has dropped slightly and

is supported by the lower leaf 56c. FIG. 8c shows the

position of the cam 56 rotated 270° wherein the upper

leaf 56b has rotated sufficiently to begin to engage the

undercut 66 of the next slide carrier while the opposite facing lower leaf 56c still supports the bottom carrier.

After a full rotation of 360' as shown in FIG. 8d, the

lower leaf 56c has rotated opposite the carrier stack and

no longer supports the bottom carrier which now rests on

the loading platform 52. At the same position, the upper

leaf 56b supports the next carrier for repeating the

cycle .

Referring again to FIG. 7a & 7b, when the carrier

drops to the loading platform 52, the contact closes

switch 90 which activates motors 86 and 87. Motor 86

drives the infeed drive belt 59 until the pusher tab 58

makes contact with the carrier and pushes the carrier

onto the X-Y stage drive belt 50. The stage drive belt

50 advances the carrier until contact is made with switch

91, the closing of which begins the slide scanning

process described further herein. Upon completion of the

scanning process, the X-Y stage 38 moves to an unload

position and motors 8, and 88 are activated to transport

the carrier to the unloading platform 36 using stage

drive belt 50. Motor 88 drives outfeed gear 93 to engage

the carrier pitch rack 68 of the carrier 60 (FIG. 6b) until switch 92 is contacted. Closing switch 92

activates motor 89 to rotate the unloading platform 36.

The unloading operation is shown in more detail in

end views of the output module 17 (FIG. 9a-9d) . In FIG.

9a, the unloading platform 36 is shown in a horizontal

position supporting a slide carrier 60. The hold-down

mechanism 94 secures the carrier 60 at one end. FIG. 9b

shows the output module 17 after motor 89 has rotated the

unloading platform 36 to a vertical position, at which point the spring loaded hold-down mechanism 94 releases the slide carrier 60 into the output hopper 18. The

carrier 60 is supported in the output hopper 18 by means

of ears 64 (FIG. 6a & 6b) . FIG. 9c shows the unloading platform 16 being rotated back towards the horizontal

position. As the platform 36 rotates upward, it contacts the deposited carrier 60 and the upward movement pushes

the carrier toward the front of the output hopper 18.

FIG. 9d shows the unloading platform 36 at the original

horizontal position after having output a series of slide

carriers 60 to the output hopper 18.

Having described the overall system and the automated slide handling feature, the aspects of the apparatus 10 relating to scanning, focusing and image processing is now described in further detail.

In some cases, an operator knows where the scan area

of interest is on the slide. Conventional preparation of

slides for examination provides repeatable and known

placement of the sample on the slide. The operator can

therefore instruct the system to always scan the same

area at the same location of every slide which is

prepared in this fashion. At other times, the area of

interest is not known, for example, where slides are

prepared manually with a known smear technique. One

feature of the invention automatically determines the scan area using a texture analysis process.

FIG. 10 is a flow diagram that describes the

processing associated with the automatic location of a

scan area. As shown in FIG. 10, the basic method is to pre-scan the entire slide area to determine texture

features that indicate the presence of a smear and to

discriminate these areas from dirt and other artifacts.

At each location of this scan, an image such as in

FIG. 12 is acquired and analyzed for texture information at steps 204 and 206. Since it is desired to locate the edges of the smear sample within a given image, texture analyses are conducted over areas called windows 78, which are smaller than the entire image as shown in FIG.

12. The process iterates the scan across the slide at steps 208, 210, 212, and 214.

In the interest of speed, the texture analysis

process is performed at a lower magnification, preferably at a 4x objective. One reason to operate at low

magnification is to image the largest slide area at any

one time. Since cells do not yet need to be resolved at this stage of the overall image analysis, the 4x

magnification is preferred. On a typical slide, as shown

in FIG. 11, a portion 72b of the end of the slide 72 is

reserved for labeling with identification information.

Excepting this label area, the entire slide is scanned in

a scan fashion 76 to yield a number of adjacent images.

Texture values for each window include the pixel

variance over a window, the difference between the

largest and smallest pixel value within a window, and other indicators. The presence of a smear raises the

texture values compared with a blank area . One problem with a smear is the non-uniform

thickness and texture. For example, the smear is likely

to be relatively thin at the edges and thicker towards

the middle due to the nature of the smearing process. To

accommodate for the non-uniformity, texture analysis

provides a texture value for each analyzed area. The

texture tends to gradually rise as the scan proceeds

across a smear from a thin area to a thick area, reaches

a peak, and then falls off again to a lower value as a

thin area at the edge is reached. The problem is then to

decide from the series of texture values the beginning

and ending, or the edges, of the smear. The texture

values are fit to a square wave waveform since the

texture data does not have sharp beginnings and endings .

After conducting this scanning and texture

evaluation operation, one must determine which areas of

elevated texture values represent the desired smear 74,

and which represent undesired artifacts. This

determination is accomplished by fitting a step function,

on a line by line basis to the texture values in step

216. This function, which resembles a single square wave

across the smear with a beginning at one edge, and end at the other edge, and an amplitude provides the means for

discrimination. The amplitude of the best-fit step

function is utilized to determine whether smear or dirt

is present since relatively high values indicate smear.

If a smear is present, the beginning and ending

coordinates of this pattern are noted until all lines

have been processed, and the smear sample area defined at

218.

After an initial focusing operation described

further herein, the scan area of interest is scanned to

acquire images for image analysis. The preferred method

of operation is to initially perform a complete scan of

the slide at low magnification to identify and locate

candidate objects of interest, followed by further image

analysis of the candidate objects of interest at high

magnification to confirm the objects as cells. An

alternate method of operation is to perform high

magnification image analysis of each candidate object of

interest; immediately after the object has been

identified at low magnification. The low magnification

scanning then resumes, searching for additional candidate

objects of interest. Since it takes on the order of a few seconds to change objectives, this alternate method of operation would take longer to complete.

The operator can pre-select a magnification level to

be used for the scanning operation. A low magnification

using a lOx objective is preferred for the scanning

operation since a larger area can be initially analyzed

for each acquired scan image. The overall detection

process for a cell includes a combination of decisions made at both low (lOx) and high magnification (40x)

levels. Decision making at the lOx magnification level

is broader in scope, i . e . , objects that loosely fit the

relevant color, size and shape characteristics are

identified at the lOx level. Analysis at the 40x magnification level then proceeds to refine the decision

making and confirm objects as likely cells or candidate

objects of interest. For example, at the 4Ox level, some

objects that were identified at lOx are artifacts which

the analysis process then rejects. In addition, closely packed objects of interest appearing at lOx are separated

at the 40x level.

When a cell straddles or overlaps adjacent image

fields, image analysis of the individual adjacent image fields could result in the cell being rejected or

undetected. To avoid missing such cells, the scanning

operation compensates by overlapping adjacent image

fields in both the x and y directions. An overlap amount

greater than half the diameter of an average cell is

preferred. In the preferred embodiment, the overlap is

specified as a percentage of the image field in the x and

y directions.

The time to complete an image analysis can vary

depending upon the size of the scan area and the number

of candidate cells, or objects of interest identified.

For one example, in the preferred embodiment, a complete

image analysis of a scan area of two square centimeters

in which 50 objects of interest are confirmed can be

performed in about 12 to 15 min. This example includes

not only focusing, scanning and image analysis but also

the saving of 4Ox images as a mosaic on hard drive 21

(FIG. 2) .

How ever the scan area is defined, an initial

focusing operation must be performed on each slide prior

to scanning. This focusing operation is required, since

slides differ, in general, in their placement in a carrier. These differences include slight (but

significant) variations of tilt of the slide in the

carrier. Since each slide must remain in focus during

scanning, the degree of tilt of each slide must be

determined. This determination is accomplished with an

initial focusing operation that determines the exact

degree of tilt, so that focus can be maintained

automatically during scanning.

The initial focusing operation and other focusing

operations to be described later utilize a focusing

method based on processing of images acquired by the

system. This method was chosen for simplicity over other

methods including use of beams reflected from the slide

surface and use of mechanical gauges . These other

methods also would not function properly when the

subsample is protected with a cover glass. The preferred

method results in lower system cost and improved

reliability, since no additional parts need be included

to perform focusing.

FIG. 13a provides a flow diagram describing the

"focus point" procedure. The basic method relies on the

fact that the pixel value variance (or standard deviation) taken about the pixel value mean is maximum at

best focus. A "brute-force" method could simply step

through focus, using the computer controlled Z or focus

stage, calculate the pixel variance at each step, and

return to the focus position providing the maximum

variance. Such a method would be too time consuming.

Therefore, additional features were added as shown in

FIG. 13a.

These features include the determination of pixel

variance at a relatively coarse number of focal

positions, and then the fitting of a curve to the data to

provide a faster means of determining optimal focus.

This basic process is applied in two steps, coarse and

fine .

During the coarse step at 220-230, the Z stage is

stepped over a user-specified range of focus positions,

with step sizes that are also user-specified. For coarse

focusing, these data are a close fit to a Gaussian

function. Therefore, this initial set of variance versus

focus position data are least-squares fit to a Gaussian

function at 228. The location of the peak of this Gaussian curve determines the initial or coarse estimate

of focus position for input to step 232.

A second stepping operation 230-242 is performed

utilizing smaller steps over a smaller focus range

centered on the coarse focus position. Experience

indicates that data taken over this smaller range are

generally best fit by a second order polynomial . Once

this least squares fit is performed at 240, the peak of

the second order curve provides the fine focus position

at 244.

FIG. 14 illustrates a procedure for how this

focusing method is utilized to determine the orientation

of a slide in the carrier. As shown, focus positions are

determined, as described above, for a fixed grid of

points centered on the scan area at 254. Should one or

more of these points lie outside the scan area, the

method senses this at 266 by venue of low values of pixel

variance. In this case, additional points are selected

closer to the center of the scan area. FIG. 15 shows the

initial array of points 80 and new point 89 selected

closer to the center. Once this array of focus positions

is determined at 268, a least squares plane is fit to this data at 270. Focus points lying too far above or

below this best-fit plane are discarded at 272 (such as

can occur from a dirty cover glass over the scan area) ,

and the data is then refit. This plane at 274 then

provides the desired Z position information for

maintaining focus during scanning.

After determination of the best-fit focus plane, the

scan area is scanned in an X raster scan over the scan

area as described earlier. During scanning, the X stage

is positioned to the starting point of the scan area, the

focus (Z) stage is positioned to the best fit focus

plane, an image is acquired and processed as described

later, and this process is repeated for all points over

the scan area. In this way, focus is maintained

automatically without the need for time-consuming

refocusing at points during scanning.

Prior to confirmation of cell objects at a 40x or

60x level, a refocusing operation is conducted, since the

use of this higher magnification requires more precise

focus than the best-fit plane provides. FIG. 16 provides

the flow diagram for this process. As may be seen, this

process is similar to the fine focus method described earlier in that the object is to maximize the image pixel

variance. This process is accomplished by stepping

through a range of focus positions with the Z stage at

276, 278, calculating the image variance at each position

at 278, fitting a second order polynomial to these data

at 282, and calculating the peak of this curve to yield

an estimate of the best focus position at 284, 286. This

final focusing step differs from previous ones in that

the focus range and focus step sizes are smaller since

this magnification requires focus settings to within 0.5

micron or better.

For some combinations of cell staining

characteristics, improved focus can be obtained by

numerically selecting the focus position that provides

the largest variance, as opposed to selecting the peak of

the polynomial. In such cases, the polynomial is used to

provide an estimate of best focus, and a final step

selects the actual Z position giving highest pixel

variance. Also, if at any time during the focusing

process at 40x or 60x the parameters indicate that the

focus position is inadequate, the system automatically

reveals to a coarse focusing process as described above with reference to FIG. 13A. This ensures that variations

in subsample thickness can be accommodated in an

expeditious manner.

For some cellular specimens and stains, the focusing

methods discussed above do not provide optimal focused

results. For example, certain white blood cells known as

neutrophils may be stained with Fast Red, a commonly

known stain, to identify alkaline phosphatase in the

cytoplasm of the cells. To further identify these cells

and the material within them, the specimen may be

counterstained with hematoxylin to identify the nucleus

of the cells. In cells so treated, the cytoplasm bearing

alkaline phosphatase becomes a shade of red proportionate

to the amount of alkaline phosphatase in the cytoplasm

and the nucleus becomes blue. However, where the

cytoplasm and nucleus overlap, the cell appears purple.

To find a best focal position at high magnification,

a focus method, such as the one shown in FIG. 13b, may be

used. That method begins by selecting a pixel near the

center of a candidate object of interest (Block 248) and

defining a region of interest centered about the selected

pixel (Block 250) . Preferably, the width of the region of interest is a number of columns which is a power of 2.

This width preference arises from subsequent processing

of the region of newest preferably using a one

dimensional Fast Fourier Transform (FFT) technique. As

is well known in the art, processing columns of pixel

values using the FFT technique is facilitated by making

the number of columns to be processed a power of two,

while the height of the region of interest is also a

power of two in the preferred embodiment, it need not be

unless a two dimensional FFT technique is used to process

the region of interest .

After the region of interest is selected, the

columns of pixel values are processed using the preferred

one dimensional FFT to determine a spectra of frequency

components for the region of interest (Block 252) . The

frequency spectra ranges from DC to some highest

frequency component. For each frequency component, a

complex magnitude is computed. Preferably, the complex

magnitudes for the frequency components which range from

approximately 25% of the highest component to

approximately 75% of the highest component are squared

and slimmed to determine the total power for the region of interest (Block 254) . Alternatively, the region of

interest may be processed with a smoothing window, such

as a Hanning window, to reduce the spurious high

frequency components generated by the FFT processing of

the pixel values in the region of interest . Such

preprocessing of the region of interest permits all

complex magnitude over the complete frequency range to be

squared and summed. After the power for a region has

been computed and stored (Block 256) , a new focal

position is selected, focus adjusted (Blocks 258, 260) ,

and the process repeated. After each focal position has

been evaluated, the one having the greatest power factor

is selected as the one best in focus (Block 262) .

The following describes the image processing methods

which are utilized to decide whether a candidate object

of interest, such as a stained cell, is present in a

given image, or field, during the scanning process.

Candidate objects of interest which are detected during

scanning are reimaged at higher (40x or 60x)

magnification, the decision confirmed, and a region of

interest for this cell saved for later review by the

pathologist . The image processing includes color space

conversion, low pass filtering, background suppression,

artifact suppression, morphological processing, and blob

analysis. One or more of these steps can optionally be

eliminated. The operator is provided with an option to

configure the system to perform any or all of these steps

and whether to perform certain steps more than once or

several times in a row. The sequence of steps can be

varied and thereby optimized for specific reagents or

reagent combinations; however, the sequence described

herein is preferred. The image processing steps of low

pass filtering, thresholding, morphological processing,

and blob analysis are generally known image processing

building blocks.

An overview of the preferred process is shown in

FIG. 17a. The preferred process for identifying and

locating candidate objects of interest in a stained

subsample on a slide begins with an acquisition of images

obtained by scanning the slide at low magnification

(Block 288) . Each image is then converted from a first

color space to a second color space (Block 290) and the

color converted image is low pass filtered (Block 292) . The pixels of the low pass filtered image are then

compared to a threshold (Block 994) and, preferably,

those pixels having a value equal to or greater than the

threshold are identified as candidate object of interest

pixels and those less than the threshold are determined

to be artifact or background pixels. The candidate

object of interest pixels are then morphologically

processed to identify groups of candidate object of

interest pixels as candidate objects of interest (Block

296) . These candidate objects of interest are then

compared to blob analysis parameters (Block 298) to

further differentiate candidate objects of interest from

objects which do not conform to the blob analysis

parameters and, thus, do not warrant further processing.

The location of the candidate objects of interest may be

stored prior to confirmation at high magnification. The

process continues by determining whether the candidate

objects of interest hare been confirmed (Block 300) . If

they have not been determined, the optical system is set

to high magnification (Block 302) and images of the slide

at the locations corresponding to the candidate objects

of interest identified in the low magnification images are acquired (Block 288) . These images are then color

converted (Block 290) , low pass filtered (Block 292) ,

compared to a threshold (Block 294) , morphologically

processed (Block 296) , and compared to blob analysis parameters (Block 298) to confirm which candidate objects

of interest located from the low magnification images are

objects of interest. The coordinates of the objects of

interest are then stored for future reference (Block

303) . In general, the candidate objects of interest, such

as stained cells, are detected based on a combination of

characteristics, including size, shape, and color. The

chain of decision making based on these characteristics preferably begins with a color space conversion process.

A camera, preferably a CCD camera, coupled to the microscope subsystem outputs a color image comprising a

matrix of 640 x 480 pixels. Each pixel comprises red,

green and blue (RGB) signal values.

It is desirable to transform the matrix of RGB values to a different color space because the difference

between candidate objects of interest and their

background, such as stained cells, may be determined from their respective colors. Subsamples are generally

stained with one or more industry standard stains ( e . g. ,

DAB, New Fuchsin, AEC) which are "reddish" in color.

Candidate objects of interest retain more of the stain

and thus appear red while normal cells remain unstained.

The subsamples may also be counter- stained with

hematoxylin so the nuclei of normal cells or cells not

containing an object of interest appear blue. In

addition to these objects, dirt and debris can appear as

black, gray, or can also be lightly stained red or blue

depending on the staining procedures utilized. The

residual plasma or other fluids also present on a smear

may also possess some color.

In the color conversion operation, a ratio of two of

the RGB signal values is formed to provide a means for

discriminating color information. With three signal

values for each pixel, nine different ratios can be

formed :

R/R, R/G, R/B, G/G, G/B, G/R, B/B, B/G, B/R

where R is red, G is green, and B is red.

The optimal ratio to select depends upon the range

of color information expected in the subsample. As noted above, typical stains used for detecting candidate

objects of interest are predominantly red, as opposed to

predominantly green or blue. Thus, the pixels of a cell

of interest which has been stained contain a red

component which is larger than either the green or blue

components. A ratio of red divided by blue (R/B)

provides a value which is greater than one for stained

cells but is approximately one for any clear or white

areas on the slide. Since the remaining cells, i.e.,

normal cells, typically are stained blue, the R/B ratio

for pixels of these latter cells yields values of less

than one. The R/B ratio is preferred for clearly

separating the color information typical in these

applications .

FIG. 17b illustrates the flow diagram by which this

conversion is performed. In the interest of processing

speed, the conversion is implemented with a look-up-

table. The use of a look up table for color conversion

accomplishes three functions: (1) performing a division

operation; (2) scaling the result for processing as an

image having pixel values ranging from 0 to 255; and (3)

defining objects that have low pixel values in each color band (R,G,B) as "black" to avoid infinite ratios ( i . e . ,

dividing by zero) . These "black" objects are typically

stalking artifacts or can be edges of bubbles caused by pasting a cover glass over the subsample.

Once the look-up-table 304 is built for the specific

color ratio (i . e . , choices of cell stains), each pixel in

the original RGB image is converted at 308 to produce the

output. In this way, the desired objects of interest can

be numerically discriminated. The resulting 640 x 480

pixel matrix, referred to as the X-image, is a gray scale

image having values ranging from 0 to 255.

Other methods exist for discriminating color

information. One classical method converts the RGB color

information into another color space, such as HSI (hue,

saturation, intensity) space. In such a space,

distinctly different hues such as red, blue, green,

yellow, may be readily separated. In addition,

relatively lightly stained objects may be distinguished

from more intensely stained ones by virtue of differing saturations. However, converting from RGB space to HSI

space requires more complex computation. Conversion to a

color ratio is faster; for example, a full image can be converted by the ratio technique of the present invention

in about 10 ms while an HSI conversion can take several

seconds .

In yet another approach, one could obtain color

information by taking a single color channel from the

camera. As an example, consider a blue channel, in which

objects that are red are relatively dark. Objects which

are blue, or white, are relatively light in the blue

channel. In principle, one could take a single color

channel, and simply set a threshold wherein everything

darker than some threshold is categorized as a candidate

object of interest, for example, a stained cell, because

it is red and hence dark in the channel being reviewed.

However, one problem with the single channel approach

occurs where illumination is not uniform. Non-uniformity

of illumination results in non-uniformity across the

pixel values in any color channel, for example, tending

to peak in the middle of the image and dropping off at

the edges where the illumination falls off. Performing

thresholding on this non-uniform color information runs

into problems, as the edges sometimes fall below the

threshold, and therefore it becomes more difficult to pick the appropriate threshold level. However, with the

ratio technique, if the values of the red channel fall

off from center to edge, then the values of the blue

channel also fall off center to edge, resulting in a

uniform ratio. Thus, the ratio technique is more immune

to illumination non-uniformities.

As previously described, the color conversion scheme

is relatively insensitive to changes in color balance,

i . e . , the relative outputs of the red, green, and blue

channels. However, some control is necessary to avoid

camera saturation, or inadequate exposures in any one of

the color bands. This color balancing is performed

automatically by utilizing a calibration slide consisting

of a clear area, and a "dark" area having a known optical

transmission or density. The system obtains images from

the clear and "dark" areas, calculates "white" and

"black" adjustments for the image processor 25, and

thereby provides correct color balance.

In addition to the color balance control, certain

mechanical alignments are automated in this process. The

center point in the field of view for the various

microscope objectives as measured on the slide can vary by several (or several tens of) microns. This results

from slight variations in position of the microscope

objectives 44a as determined by the turret 44 (FIG. 4) ,

small variations in alignment of the objectives with

respect to the system optical axis, and other factors.

Since it is desired that each microscope objective be

centered at the same point, these mechanical offsets must

be measured and automatically compensated.

This compensation is accomplished by imaging a test

slide which contains a recognizable feature or mark. An

image of this pattern is obtained by the system with a

given objective, and the position of the mark determined.

The system then rotates the turret to the next lens

objective, obtains an image of the test object, and its

position is redetermined. Apparent changes in position

of the test mark are recorded for this objective. This

process is continued for all objectives.

Once these spatial offsets have been determined,

they are automatically compensated for by moving the

stage 38 by an equal (but opposite) amount of offset

during changes in objective. In this way, as different lens objectives are selected, there is no apparent strife

in center point or area viewed.

A low pass filtering process precedes thresholding.

An objective of thresholding is to obtain a pixel image

matrix having only candidate objects of interest, such as

stained cells above a threshold level and everything else

below it. However, an actual acquired image contains noise. The noise can take several forms, including white

noise and artifacts. The microscope slide can have small

fragments of debris that pick up color in the staining

process and these are known as artifacts. These artifacts are generally small and scattered areas, on the

order of a few pixels, which are above the threshold.

The purpose of low pass filtering is to essentially blur

or smear the entire color converted image. The low pass

filtering process smears artifacts more than larger

objects of interest, and thereby eliminate or reduce the

number of artifacts that pass the thresholding process.

The result is a cleaner thresholded image downstream.

In the low pass filter process, a 3 x 3 matrix of

coefficients is applied to each pixel in the 640 x 480 x-

image . A preferred coefficient matrix is as follows: 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9

At each pixel location, a 3 x 3 matrix comprising the

pixel of interest and its neighbors is multiplied by the

coefficient matrix and summed to yield a single value for

the pixel of interest .

The output of this spatial convolution process is

again a 640 x 480 matrix.

As an example, consider a case where the center

pixel and only the center pixel, has a value of 255 and

each of its other neighbors, top left, top, top right and

so forth, have values of 0. This singular white pixel

case corresponds to a small object. The result of the

matrix multiplication and addition using the coefficient

matrix is a value of 1/9 (255) or 28 for the center

pixel, a value which is below the nominal threshold of

128. Now consider another case in which all the pixels

have a value of 255 corresponding to a large object.

Performing the low pass filtering operation on a 3 x 3

matrix for this case yields a value of 255 for the center

pixel. Thus, large objects retain their values while

small objects are reduced in amplitude or eliminated. In the preferred method of operation, the low pass filtering

process is performed on the X image twice in succession.

To separate objects of interest, a thresholding

operation is performed designed to set pixels within

cells of interest to a value of 255, and all other areas

to 0. Thresholding ideally yields an image in which

cells of interest are white and the remainder of the

image is black. A problem one faces in thresholding is

where to set the threshold level . One cannot simply

assume that cells of interest are indicated by any pixel

value above the nominal threshold of 128. A typical

imaging system may use an incandescent halogen light bulb

as a light source. As the bulb ages, the relative

amounts of red and blue output can change. The tendency

as the bulb ages is for the blue to drop off more than

the red and the green. To accommodate for this light

source variation over time, a dynamic thresholding

process is used whereby the threshold is adjusted

dynamically for each acquired image. Thus, for each 640

x 480 image, a single threshold value is derived specific

to that image . As shown in FIG. 18, the basic method is to calculate, for each field, the mean X value, and the

standard deviation about this mean at 312. The threshold is then set at 314 to the mean

plus an amount defined by the product of a (user

specified) factor and the standard deviation of the color

converted pixel values. The standard deviation

correlates to the structure and number of objects in the

image. Preferably, the user specified factor is in the

range of approximately 1.5 to 2.5. The factor is

selected to be in the lower end of the range for slides in which the stain has primarily remained within cell boundaries and the factor is selected to be in the upper

end of the range for slides in which the stain is

pervasively present throughout the slide. In this way,

as areas are encountered on the slide with greater or

lower background intensities, the threshold may be raised

or lowered to help reduce background objects. With this method, the threshold changes in step with the aging of

the light source such that the effects of the aging are

canceled out. The image matrix resulting at 316 from the thresholding step is a binary image of black (0) and

white (255) pixels.

As is often the case with thresholding operations

such as that described above, some undesired areas lies

above the threshold value due to noise, small stained

cell fragments, and other artifacts. It is desired and

possible to eliminate these artifacts by virtue of their

small size compared with legitimate cells of interest.

Morphological processes are utilized to perform this

function.

Morphological processing is similar to the low pass

filter convolution process described earlier except that

it is applied to a binary image. Similar to spatial

convolution, the morphological process traverses an input

image matrix, pixel by pixel, and places the processed

pixels in an output matrix. Rather than calculating a

weighted sum of neighboring pixels as in the low pass

convolution process, the morphological process uses set

theory operations to combine neighboring pixels in a

nonlinear fashion.

Erosion is a process whereby a single pixel layer is

taken away from the edge of an object. Dilation is the opposite process which adds a single pixel layer to the

edges of an object. Fine power of morphological

processing is that it provides for further discrimination

to eliminate small objects that have survived the

thresholding process and yet are not likely the cells of interest. The erosion and dilation processes that make

up a morphological "open" preferably make small objects

disappear yet allows large objects to remain.

Morphological processing of binary images is described in

detail in Digi tal Image Processing (G.A. Saxes, John

Wiley & Sons, 1994, pp. 127-137) .

FIG. 19 illustrates the flow diagram for this

process. As shown hex, a morphological "open" process

performs this suppression. A single morphological open

consists of a single morphological erosion 320 followed

by a single morphological dilation 322. Multiple "opens"

consist of multiple erosions by multiple dilations. In

the preferred embodiment, one or two morphological opens are found to be suitable.

At this point in the processing chain, the processed

image contains thresholded objects of interest, such as

stained cells (if any were present in the original image) , and possibly some residual artifacts that were

too large to be eliminated by the processes above.

FIG. 20 provides a flow diagram illustrating a blob

analysis performed to determine the number, size, and

location of objects in the thresholded image. A blob is

defined as a region of connected pixels having the same

"color", in this case, a value of 255. Processing is

performed over the entire image to determine the number of such regions at 324 and to determine the area and x,y

coordinates for each detected blob at 326.

Comparison of the size of each blob to a known

determined area at 328 for a stained cell allows a

refinement in decisions about which objects are objects of interest, such as stained cells, and which are

artifacts. The location (x,y coordinates) of objects

identified as cells of interest in this stage are saved

for the final 4Ox reimaging step described below. Objects not passing the size test are disregarded as

artifacts . The processing chain described above identifies

objects at the scanning magnification as cells of

interest candidates. As illustrated in FIG. 21, at the completion of scanning the system switches to the 4Ox

magnification objective at 330, and each candidate is

reimaged to confirm the identification 332. Each 40x

image is reprocessed at 334 using the same steps as

described above but with test parameters suitably

modified for the higher magnification ( e . g. , area). At

336, a region of interest centered on each confirmed cell

is saved to the hard drive for review by the pathologist.

As noted earlier, a mosaic of saved images is made available for viewing by the pathologist. As shown in

FIG. 22, a series of images of cells which have been

confirmed by the image analysis is presented in the

mosaic 150. The pathologist can then visually inspect

the images to make a determination whether to accept (152) or reject (153) each cell image. Such a

determination can be noted and saved with the mosaic of

images for generating a printed report.

In addition to saving the image of the cell and its region, the cell coordinates are saved should the

pathologist wish to directly view the cell through the oculars or on the image monitor. In this case, the pathologist reloads the slide carrier, selects the slide and cell for review from a mosaic of cell images, and the

system automatically positions the cell under the

microscope for viewing.

Normal cells whose nuclei have been stained with

hematoxylin are often quite numerous, numbering in the thousands per lOx image. Since these cells are so

numerous, and since they tend to clump, counting each

individual nucleated cell would add an excessive

processing burden, at the expense of speed, and would not

necessarily provide an accurate count due to clumping. The apparatus performs an estimation process in which the

total area of each field that is stained hematoxylin blue

is measured and this area is divided by the average size

of a nucleated cell. FIG.' 23- outlines this process.

In this process, a single color band (the red

channel provides the best contrast for blue stained

nucleated cells) is processed by calculating the average pixel value for each field at 342, establishing two

threshold values (high and low) as indicated at 334, 346,

and counting the number of pixels between these two

values at 348. In the absence of dirt, or other opaque debris, this provides a count of the number of

predominantly blue pixels. By dividing this value by the

average area for a nucleated cell at 350, and looping

over all fields at 352, an approximate cell count is

obtained. Preliminary testing of this process indicates

an accuracy with +/- 15%. For some slide preparation techniques, the size of nucleated cells can be

significantly larger than the typical size. The operator

can select the appropriate nucleated cell size to

compensate for these characteristics.

As with any imaging system, there is some loss of

modulation transfer ( i . e . , contrast) due to the

modulation transfer function (MTF) characteristics of the

imaging optics; camera, electronics, and other

components. Since it is desired to save "high quality"

images of cells of interest both for pathologist review

and for archival purposes, it is desired to compensate for these MTF losses.

An MTF compensation, or MTFC, is performed as a

digital process applied to the acquired digital images.

A digital filter is utilized to restore the high spatial

frequency content of the images upon storage, while maintaining low noise levels. With this MTFC technology,

image quality is enhanced, or restored, through the use

of digital processing methods as opposed to conventional

oil-immersion or other hardware based methods. MTFC is

described further in The Image Processing Handbook (J.C. Rues, CRC Press, 1995, pp. 225 and 337) .

Referring to FIG. 24, the functions available in a

user interface of the apparatus 10 are shown. From the

user interface, which is presented graphically on computer monitor 26, an operator can select among

apparatus functions which include acquisition 402,

analysts 404, and system configuration 406. At the

acquisition level 402, the operator can select between

manual 408 and automatic 410 modes of operation. In the manual mode, the operator is presented with manual

operations 409. Patient information 414 regarding an

assay can be entered at 412. In the analysis level 404,

review 416 and report 418 functions are made available. At the review level 416, the operator can select a montage function 420. At this montage level, a

pathologist can perform diagnostic review functions

including visiting an image 422, accept/reject of cells 424, nucleated cell counting 426, accept/reject of cell

counts 428, and saving of pages at 430. The report level

418 allows an operator to generate patient reports 432.

In the configuration level 406, the operator can

select to configure preferences at 434, input operator

information 437 at 436, create a system log at 438, and

toggle a menu panel at 440. The configuration

preferences include scan area selection functions at 442,

452; montage specifications at 444, bar code handling at

446, default cell counting at 448, stain selection at

450, and scan objective selection at 454.

Computer Implementation

Aspects of the invention may be implemented in

hardware or software, or a combination of both. However,

preferably, the algorithms and processes of the invention

are implemented in one or more computer programs

executing on programmable computers each comprising at

least one processor, at least one data storage system

(including volatile and non-volatile memory and/or

storage elements) , at least one input device, and at

least one output device. Program code is applied to input data to perform the functions described herein and

generate output information. The output information is

applied to one or more output devices, in known fashion.

Each program may be implemented in any desired

computer language (including machine, assembly, high

level procedural, or object oriented programming

languages) to communicate with a computer system. In any

case, the language may be a compiled or interpreted

language .

Each such computer program is preferably stored on a

storage media or device ( e . g. , ROM, CD-ROM, tape, or

magnetic diskette) readable by a general or special

purpose programmable computer, for configuring and

operating the computer when the storage media or device

is read by the computer to perform the procedures

described herein. The inventive system may also be

considered to be implemented as a computer-readable

storage medium, configured with a computer program, where

the storage medium so configured causes a computer to

operate in a specific and predefined manner to perform

the functions described herein. A number of embodiments of the present invention

have been described. Nevertheless, various modifications

may be made without departing from the spirit and scope of the invention. Accordingly, the invention is not to

be limited by the specific illustrated embodiment, but

only by the scope of the appended claims.

Claims

CLAIMSWe claim:

1. An automated method for the measurement of residual

protein in a treated cellular specimen, comprising:

(a) providing a plurality of stained subsamples

from a cellular specimen, wherein

(1) at least one of the subsamples has been

treated before being stained;

(2) at least one of the samples has not been

treated; and

(3) the untreated subsample and the treated

subsample had been stained together;

(b) automatically selecting a Z position in each

subsample for a candidate object of interest;

(c) automatically obtaining a low magnification

image of the candidate objects of interest in

each subsample;

(d) automatically filtering the candidate object of

interest pixels in each subsample with a low

pass filter; (e) automatically morphologically processing the

candidate object of interest pixels in each

subsample to identify artifact pixels;

(f) automatically identifying the candidate objects

of interest in each subsample from the

remaining candidate object of interest pixels

in the subsample not identified as artifact

pixels;

(g) adjusting the apparatus to a higher

magnification;

(h) automatically acquiring a higher magnification

image of the subsample, at the location

coordinates corresponding to the low

magnification image, for each candidate object

of interest;

(i) automatically transforming pixels of the higher

magnification image in the first color space to

a second color space to differentiate higher

magnification candidate objects of interest

pixels from background pixels;

(j) automatically identifying, at high

magnification, objects of interest from the candidate object of interest pixels in the

second color space;

(k) scoring the protein level in the untreated

subsamples;

(1) scoring the protein level in the treated

subsamples; and

(m) determining

Δ = [protein level in the treated

subsamples] / [protein level in the untreated

subsamples] , wherein Δ is a measurement of the

residual component of a cellular protein.

2. The method of claim 1, wherein the first color space

includes red, green, and blue components for each

pixel and the transforming step includes forming a

ratio between two components of the red, blue and

green components for each pixel in the first color

space to transform the pixels to the second color

space .

3. The method of claim 1, wherein the first color space

includes red, green, and blue components for each pixel and the transforming step includes converting

components of the red, blue and green components for each pixel in the first color space to pixel values

in a hue, saturation, and intensity space.

4. The method of claim 1, wherein the first color space includes red, green, and blue components for each

pixel and the transforming step includes comparing

pixel values for a single component for each pixel

to a threshold to identify pixels having a component value equal to or greater than said threshold as

candidate object of interests pixels and pixels

having a component value less than the threshold as background pixels.

5. The method of claim 1, wherein the treatment is

selected from the group consisting of acid, base,

high salt, low salt, urea, heat, detergent, and incubation with an enzyme inhibitor.

6. The method of claim 1, wherein the cellular protein

is an enzyme.

7. The method of claim 6, wherein the enzyme is

alkaline phosphatase (AP) .

8. The method of claim 7, wherein the treatment is

selected from the group consisting of urea and heat.

9. The method of claim 6, wherein the enzyme is acid

phosphatase (AP) .

10. The method of claim 9, wherein the treatment is

incubation with tartrate.

11. The method of claim 6, wherein the enzyme is

α-naphthyl butyrate esterase.

12. The method of claim 11, wherein the treatment is

incubation with fluoride.

13. The method of claim 1, wherein the cellular protein

is assayed immunologically.

14. The method of claim 1, wherein the image is a color

image .

15. The method of claim 1, wherein the image is a

digital image.

16. A computer program, residing on a computer-readable

medium, for obtaining images of subsamples of a

cellular specimen, the computer program comprising

instructions for causing a computer to:

(a) select a Z position in each treated and

untreated subsample for candidate object of

interest;

(b) obtain a low magnification image of the

candidate object of interest in each subsample;

(c) filter the candidate object of interest pixels

in each subsample with a low pass filter;

(d) morphologically process the candidate object of

interest pixels in each subsample to identify

artifact pixels;

(e) identify the candidate object of interest in

each subsample from the remaining candidate object of interest pixels in the subsample not

identified as artifact pixels;

(f) adjust the apparatus to a higher magnification;

(g) acquire a higher magnification image of the

subsample, at the location coordinates

corresponding to the low magnification image,

for each candidate object of interest;

(h) transform pixels of the higher magnification

image in the first color space to a second

color space to differentiate higher

magnification candidate objects of interest

pixels from background pixels;

(i) identify, at higher magnification, object of

interest from the candidate object of interest

pixels in the second color space;

(j) score a protein level in the untreated

subsamples;

(k) score a protein level in the treated

subsamples; and

(1) calculate a Δ = [protein level in the treated

subsamples] / [protein level in the untreated subsamples] , wherein Δ is a measurement of the residual component of a

cellular protein.