CA1135979A - Automated method and apparatus for classification of cells with application to the diagnosis of anemia - Google Patents

Abstract

AUTOMATED METHOD AND APPARATUS
FOR CLASSIFICATION OF CELLS
WITH APPLICATION TO
THE DIAGNOSIS OF ANEMIA

ABSTRACT

A method and apparatus are disclosed for mea-suring characteristics of cells, such as red blood cells, and for analyzing parameters of the cell characteristics to define a patient's blood. These parameters may be com-pared for resemblance to predetermined reference charac-teristic values for a blood cell pathological condition such as a specific kind of anemia or for a normal blood.
A report may be generated showing such resemblance to an anemia or to a normal blood. A report may generated showing parameters of a multivariate dispersion of distri-bution for a subpopulation of biconcave cells, an indica-tion of skewness of the distribution of the cells with regard to shape variations in central pallor size; the proportion of abnormal kinds of cells found, and close-ness of blood to several specific anemias. To expedite the system, a plurality of microprocessors are employed with one microprocessor controlling the imaging means and the summing of measured characteristics while one or more additional microprocessors are measuring characteristics and analyzing the digitized image signals. An improved method of measuring central pallor size is provided.

Description

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AUTOMATED METHOD AND APPARATUS :~
i FOR CLASSIFICATION OF CELLS
WITH APPLICATION TO
' THE DIAGNOSIS OF ANEMIA

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,. ~, This invention relates to an apparatus for .. automatically analyzing blood, and through the accumula-`, ~ tion of measured properties from individual cells, of thereby classifying the blood specimen according to its ~: close resemblance to the normal or to various pathological ~: ~ conditions. More particularly it is concerned with automatically classifying red blood cells and by accumu-. lating measurements relative to each cell generating ''~ : characteristic values, which by automated means identify -: 10 the given blood specimen as typical of either normal or of the pathological condition of a specific type of red cell disorder or anemic condition.
;; According to present medical practice, the diagnosis of a particular one of more than a dozen major ~:
l types o~ anemia uses three broad categoriesi of infoxma-~L~3~
; - 2 -` tion: 1) mean descriptors of c~ll number, size and hemoglobin content, 2) subjective microscopic visual evaluation of the stained blood cells by a trained hemotologist, and 3~ specific biochemical or other tests to pinpoint the precise cause of the anemia.
With conventional equipment, the most common of the first category or red cell sample descriptors are 1) the red cell count, or the number of red cells ~ per unit volume of blood, 2) lhe hemoglobin content, or `; 10 amount of hemoglobin per unit volume of blood, 3) the packed cell volume, or the percentage of blood occupied by red cells, 4) a mean cell size parameter, usually taken as the mean cell volume, which is derived by dividing the packed cell volume by the red cell count, 5) a mean cel] hemoglobin parameter, which is derived by dividing the total blood hemoglobin content by the red cell count, and 6) the mean cell hemoglobin concen~
`~ tration, which is derived by dividing the total cell hemoglobin by the packed cell volume.
In the second category, a subjective visual evaluation of the stained blood specimen relates to the tedious and time consuming process by a hemotologist of examining a blood film under the microscope and identify-ing characteristic abnormal cells, such as large cells, or macrocytes, small cells such as microcytes, target cells, elongated cells such as sickle cells, and giving estimates of size variation e.g. anisocytosis +1, +2, or +3, or a subjective evaluation of population shape ;~ changes, such as poikilocytosis +1, ~2, or ~3.
Today, in addition to the overall mean red cell parameters, and the visual descriptions, certain biochem-ical and other sophisticated tests are often performed to further clarify the pathology of the anemia. These include: iron kinetics tests, serum iron level tests, hemoglobin electrophoresis, folic acid level tests, , .

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_ 3 _ -vitamin B12 tests, ancl the extraction of a bone mairrow . sample for evaluation of maturation changes and stainable ixon levels~ These are ver~ time consuming and expensive, and as to the extraction of.a ~one marxow sample, very pain~ul.
In accoraaince with the above, one aspect of the present invention replaces the tedious visual examination with an automatic classification of the noxmal and abnor-mal xe~ blood cells into sub-~populations'and extracts .
meaningful red blood cells parameters for the sepaxate sub-populations. These and other parameters are used for the automa~ic classification o~ the blood specimen with xespec-t to the categoxies of anemia. One dif~iculty encoun'ter~d in separating the noxmal and abnormal cells into meaningful and ~idely recognized sub-popula.tions' on an automated '' basis is that of accurately segregating the cells by ~heir morpholog~ and color, particularly where their ' . xespective axeas ox sizes ana shapes overlap and their respecti~e principal distinguishing featuxe is the 20 . configuration ~f their respective central pallors ~or a lack o central pallor). Centxal pallor is the thin, disc-shaped central area of red blood cells which may be circular and particularly pronounced for some cells . For instance, target cells and norm~c~tes ma~ ha~e sub- :
stantially the same size area and shape, but di~fer in a central pallor configura-tion Thus, to distinguish between these cells, and subsequently to ~istinguish between anemias, the automated analysis should be able to examine and classify cells on the basis of their interior confi~uration, as well as their exterior confi~urations. ;~
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, Other ~ells, such as spiculed red blood cells, may have the same general size, area and interior configurations of normocytes or the like, but are dis-tinguished principally by their indented spiculed perimeters. Likewise, adding to the difficulty of classifying abnormal cells such as sickle cells from other elongated cells is that they may have similar peripheral measurements, sizes, and areas, but differ principally from one another in the presence of pointed projections, or spicules. Still other abnormal cells may be separately categorized from other morphologically similar cells only by their hemoglobin content, measured in terms of color or density. Therefore, it may be desirable to distinyuish the hypochromic cells from those that are normochromic.
It has been further discovered that the classi-fication of the red cells into subpopulations is not always discerning enough with regard to automatic anemia - classification, but that further statistical clescriptions of these subpopulations are often required. For example, di~ferent anemia bloods may have the same percentage of round biconcave cells but show considerable variation in the dispersion of the cells with regard to size, hemo-~ globin content and central pallor. Other anemias may - 25 result in blood with the same percentage of elongated or spiculed cells, but differ signi~icantly with rega.rd to measure of mean size, hemoglobin, or with regard to the total population skewness relative to a shape measure.
As will be explained in greater detail herein-after, the present invention is described in connectionwith a microscopic slide, digital image and pattern recognition system. However, the invention is not to be construed as limited to such a system, as the feature analysis o~ the sample to classify the blood and to test - 35 the blood for abnormalities may ba performed using other techniques, such as a coherent optical analysis technique ., , , .,: , . ~ . . . . .

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disclosed in U.S. Patent No. 3,947,123; or a liquid flow process technique such as disclosed in U.S. Patent Nos. 3,819,270 and 3,822,095. l'o be commercially feasible, the digital image and pattern recognition process for the blood cells should operate on a real time basis and with sufficient speed and accuracy that it will perform as well AS the now commercially accepted leukocyte differential counting system, such as for example, the LARC manufactured by Corning Glass Works of Corning, New York, and generally disclosed in U.S. Patent No. 3,883,852.
The Coulter Counter, manufactured by Coulter Electronics, Hialeah, Florida, provides results which are helpful in diagnosing anemia in that it provides a red blood cell count and mean red blood cells parameters characterizing the entire population of cells; more specifically, the Wintrobe indices of mean cells volume, mean cell hemoglobin and mean cell hemoglobin concentra-tion along with the number of red cells per cubic milli-meter. However, no differentiation between abnormal ornormal red blood cells is achieved with the Coulter Counter. Furthermore, the hemoglobin content for individ-ual cells is not determined and measures of dispersion and skewness are not performed. Finally, no automatic categorization or quantitative direct association with known anemias or other pathologies is automatically included as part of the analysis.
Heretofore, some offline experimental work has been performed on image processing of erythrocytes. One of these works, "Bentley, S.A. and S. M. Lewis, The Use of an Image Analyzing Computer for the Quantification of ~ed Cell Morphological Characteristics, Brit. J. Haemat.
29:81, 1975", describes an offline analysis of dried and stained red b]ood cells of a total cell population measuring three red blood cells parameters by an image ` analysis technique. This analysis is similar to the ~3~

Coulter Counter analysis in that the parameters measured were from the total population of cells being analyzed, and were analogous to the Wintrobe Indices. The drying of the red cells introduced artifacts, and there was a lack o~ central pallor, or internal red cell analysis to provide a highly refined classification. Moreover, there was not disclosed the capability for differentiating between and classifying normal red blood cells from abnor-mal red blood cells. Likewise, there was no capability disclosed for the automatic classification of the red blood cells with respect to categories of anemias.
Measurements of normal erythrocytes without differentiation of any abnormal erythrocytes by image processing has been disclosed by ~. E. Green and reported in a paper entitled "Green, J. E., 'Computer Methods For Erythrocytes Analysis', Proceedings of Symposium of Feature Extraction and Selection and Pattern Recognition, IEE Catalog No. 70C 51C pp. 100, Argonne, Illinois, 1~70".
A similar type of paper reporting measurements on the red cells and how to measure their features without any classification thereof was disclosed in a paper entitled "Eden, N., 'Image Processing Techniques in Relation to Studies of ~ed Cell Shape', edited by ~. Bessis, R. Weed ; and Leblond, Springer-Verlag. New York, pp. 141, 1973".
In Patent No. 3,851,156 Green provides a tech-nique for scene segmentation of stained red and white , blood cells through the use of a color algebra technique.
In so doing features of perimeter, siæe and color are ` generated for red blood cells. These are measured on the total population of cells and no classification into sub-populations is performed. Further a precise central pallor analysis is not considered, and means are not ~` provided to acquire subpopulation statistical measures, such as the bivariate dispersion of the hemoglobin and size, nor is it indicated that they are important to achieve an anemia categorization, or a profile of similarity measures to prototype anemias.
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In short, none of the aforementioned systems have the abi]ity to analyze cells by their features, particularly the inner features of cell pallor, to quickly classify the blood sample or report it as similar~
~ith the method and apparatus disclosed in my above-identified co-pending patent application, a quanti-tative analysis of abnormal subpopulations of cells has been performed on a scale heretofore not possible.
Because this equipment and techniques enable analysis of cells more quickly and accurately than can the human eye or other existing equipment, it has been possible to yain ; a better quantitative understanding of abnormal red blood cell subpopulations for different anemias and the relation-ship of the abnormal red blood cell subpopulations to normal subpopulations. It has now been found that a red blood cell sample of blood contains significantly more information concernin~ the type of anemia present than heretofore known or thought.
Thus, with the present invention, it has been possible to quantify and identify blood from patients with anemia by variables or measures of characteristic values of subpopulations, such as size, hemoglobin content, percentages of cells, and other parameters such as measures of dispersion and skewness for certain single and combined parameters over different subpopulations as well as the population of cells as a whole. These measured properties of the subpopulation of cells and the population as a whole provide a robust description of a patient's blood sample.
~ 30 Also, with the present invention, it is now i~ possible to compare these red blood cells descriptors ; relative to characteristic values for the red blood cells in a standard normal blood sample and ~o those typically found in each of a plurality of recognized kinds of anemia.
It is also possible to ~enera~e an indice of the relative ~` closeness of the blood sample to one or more standard ~` anemias so that the clinician is given a powerful quanti-',' -.~
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tative relationship to aid in his diagnosis, Addition-ally, by testing the patient's blood at different times, particularly after successive treatments, one can generate a series of indices of the patient's blood relative to each type of anemia. Thus, it is possible to determine a patient's progress to see if his blood is deteriora~
ting or is progressing towards a more normal blood.
With expanded and accepted usage of the present inve~ion, it is thought that some of the other ti.me consuming, painful and/or expensive tests, above discussed, and now commonly used in the diagnosis of anemia, may be elimi-nated. Therefore, and as will be explained in greater detail hereinafter, a real time analysis of the red blood cell sample and to determine its makeup and a comparison to standard types of anemia is feasible.
To this end, the present invention uses multiple logic systems opera-ting simultaneously and in a controlled rela~ionship one with another to divide and perform tasks therebetween. Thus, the ability to analyze hundreds of red blood cells and to extract their various features and then to define the parameters for the subpopulations for ,~ comparison with anemia standards can be accomplished by the division of the functions and tasks between these simultaneously cooperating logic systems, all as will be explained in greater detail hereinafter.
A general object of the invention is to provide a new and improved, as contrasted to the prior art, syste~
for the automatic analysis and classification of cells.
An object of the invention is to provide a method and apparatus for automatically classifying the blood and its relationship to recogni~ed categories of anemias.
Another object of the invention is to prov.ide an improved method and apparatus for automatically testing blood for abnormalities.
These and other objects of the invention will ; become apparent from the following detailed description and the accompanying drawings in which: -~` , -: - . ~ , . : , . , .. . .. . : ,.

FIGURE 1 is a perspective view of an apparatus for practicing the method of blood analysis and embo~ying novel features of the invention;
~ IGURE 2 is a block diayram showing the opera-tion of the apparatus illustrated in Figure l;
FIGURE 3 is a block diagram of the preferred process for analyzing and classifying blood cells;
FIGURE 4 illustrates a scanning technique for locating cells and determining the boundary points of cells in an image;
FIGURES 5a, 5b, and 5c are flow charts o~ the pr~ferred classification technique for classifying the blood cells into mutually exclusive subpopulations.
FIGURE 6 is a diagrammatic view of a model for red blood cell central pallor measurement.
FIGURE 7, located adjacent FIG. 4, illustrates a chain code i description and analysis method for three diagrammatic red blood cell I outlines;
~IGURE 8, adjacent FIG. 7, is a block diagram of the preferred prccess for determining whether a cell is round;
~ IGURES 9a, 9b, and 9c are graphs illustrating thickness~density profile measurements for three differ-ent, typically appearing cell types, meas~ed in two orthogonal directions. These profiles are used to measure - 25 the cell central pallor features and target cell features, ~igure 9a illustrating a "flat" cell having little or no central pallor developmenti FIG~RES lOa, lOb, and lOc are graphs illustrating ; the profiles of the cells of Figures 9a, 9b, and 9c with the peaks and valleys of each profile labelled;
I FIGURE 11 is a schematic of the preferred l! , process for accumulating red blood cell subpopulations parame~ers;
~IGURES 12a, 12b, 12c, 12d, and 12e are schematics illustrating the preferred process of com~lting the subpopulation characteristics from the accumulated values from a plurality of cells; -'.' ..

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~ IGURES 13a, 13b, 13c, and 13d, located adjacent FIG. 6, are graphs of bivclriate distributions o~ red blood cells subpopulations exhibiting normal and anemic characteristics;
~IGURE 14 is a schematic block diagram of the preferred process for generating a similarity,or n-space distance, measure between a given set of measurement values describing a blood and a stored set of characteris-tic values, prototypic of various anemic condition6, or the normal blood.
FIGURE 15 is a graph of population distributions of the cell circularity shape measure, illustrating the differences in skewness of these distribu-tions, over all subpopulations, for normal blood compared *o that of sickle cell anemia.
FIGURE 16 is a graph of population distributions of individual cell central pallor measurements, for the two subpopulations, biconcave and spheroc~te, illust~ating - the differences in mean values and dispersion for bloods of spherocytosis, normal and iron deficiency anemia.
As shown in the drawings for purposes of illus- -tration, the invention is embodied in a method and appa-ratus for automatically classifying red blood cells and ~or analyzing the relationship of the patient's blood sample to at least one recognized category of anemia or to a noxmal blood, or to a red blood cell disorder other than an anemia. More specifically, individual red blood cells are automatically examined and classified into different cell subpopulations such as, for example, a spherocytic cell subpopulationr an elongated cell sub-30 population, an irregular shape cell subpopulation, a -target celI subpopulation and a generally round and ~`~ biconcave cell subpopulation, and then a plurality of characteristic values are generated for the patient's subpopulations and population of cells as a whole for comparison with reference characteristic ~alues which define a recognized anemia. By wa~ of example, the selected chaxacteristic ~alues, which identify a given n~

anemia, have been found and are given hereinafter for the following anemias: iron deficiency, chronic disease, B-thalassemia, megaloblastic, hemoglobin SS, hemoglobin SC and spherocytic; and likewise, refexence character-istic values defining a normal blood, i.e., substantiallyall normocytic cells or the like, have been de~eloped and will be given hereinafter.
Also, as will be explained in greater detail hereinafter, a population or subpopulation dispersion measure of the red blood cells in a patient's blood relative to characteristics such as, for example, hemo-globin, mean cell size (or area) shape, and central pallor, may be reported to the clinician. For example, broadly speaking, the bivariate red cell distribution of size and hemoglobin content for each cell is generally in the form of an elliptically shaped profile, as best seen in FIGURES 13a-13d and having axes at 45 and 135. The length and width as measures of the bivariate dispersion, and the location of the profile by measures of the mean values, may be reported to provide the clinician with an impression of the patient's total cell makeup. Similarly, measures of central tendency dispersion and skewness of pallor and shape are provided to further quantify the total cell makeup on the total cell population, or on subpopulations, as illustrated in FIGURES 15 and 16.
The patient who has anemia generally is experi-encing difficulty in either manufacturing new normocytic red blood cells or his existing red blood cells are being destroyed at an abnormal rate or by an abnormal process.
Red blood cells typically have a life o about 120 days ; and their generation, growth, and death is a continuous process. An anemic disorder generally manifests itself in blood cells haviny unusual sizes or s~apes relative to a nor~al red blood sample, which predo~nantly contains round normocytic red blood cells, or i~ ~lood cells having hemoglobin charactexistics differing fx~ the hemoglobin characteristics for normal blood cells. Thus, since the ' ' ~ . . ' . ', .. . ' ' ~.3~'~37'~

I currently existing cells, for example in a normal patient ; who has just developed an underlying disease process leading to anemia, have a life span of 120 days, new cells with differing characteristics will tend to produce a wider dispersion of population measurements, as in Figure 13c compared to Figure 13a, when sampled and examined by the precise measurement techniques described hexein. Such information provides the clinician with a knowledge of the presence of any previously visually estimated anisocytosis, i.e., a large measure of cell variations in size, and also as well the variations in hemoglobin content. The mean cell hemoglobin and mean ~ell size information locate the central tendency of the cell di$tributions in Figures 13a-13d.
In the embodiment of the invention described herein, each of the specified anemias is identified by 16 stored parameters or properties. The patient's blood is analyzed on an individual cell basis with each cell being classified into a subpopulation and then parameters such

2~ as mean cell size, mean cell hemoglobin, and the percen-t-age of cells in the subpopulation of the total cell popu-lation are generated to give subpopulation results. Also, a plurality of other measured properites or parameters of the patient's blood are generated from the subpopulation parameters, to total 16 parameters to define a set of reference characteristic values, i.e., an n-space location, and a calculation is made of the closeness of the patient's blood location relative to the eight reference character values or n-space locations for the seven anemias and the normal blood. A report of the closeness of the patient's blood sample relative to these standard anemias provides the clinician with a statement as to what type of anemia, iE any, the patient has, or how similar it is to a known type. Then, after the patient1s treatment, the clinician is able to make later analyses and achieve new quantified results showing whether the patient is progressing towards a more normal blood or is deteriorating.
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~ ~t;~ 3 To achieve the analysis of the individual cells and the classification of same into subpopulations and the comparison of the blood subpopulations variables to `; those defining a specific anernia on a real time basis, the preferred equipment employs first and second logic systems which operate simultaneously and in a controlled manner so as to proportion the work and efforts there-between. Also, as will be explained in greater detaii, ; the present apparatus and method include a number of power*ul and novel techniques and means of and for cell classifying and analyzing which result in an e~ficient and less expensive method and apparatus for doing the red blood cell analysis. For instance, the present invention ~- recognizes that a normal blood sample generally will have a very high percentage of round cells with identifiable central pallor which can be grouped into a common sub-population called a "biconcave" cell subpopulation and that seven different anemias can be identified when using only ~our other subpopulations with this biconcave sub-population. It is to be understood, however, that the ` present invention is not limited to any subpopulations described or defined herein, as the particular names and ~` makeup of subpopulations may be varied and still fall within the purview of the invention herein claimed.
; 25 As shown in Figures 1 and 2 of the drawings, for ` purposes of illustration, the invention is embodied in an apparatus 10 which comprises a microscopic digital image processing and pattern recognition system which analyzes a mono layer of red blood cells on a microscope slide 12 with the cells being spaced from each other to ease theautomated clas~sification thereof. Suitable high res~lution microscope optics 1~ form an aptical image for each red blood cell on a vidicon television camera tube or other detector 16 which converts the scanned electronic charged distribution of the optical image point by point into a numerical or digitized image representing the optical transmission of the points in each image. The ou-tput of f ~

I the vidicon camera is applied to digitizer electronics 20 which includes an analog to digital-converter which is connected to an image process:ing logic 22 which controls the digitizer electronics 20 and receives and stores the digitized cell images into a memory store. The image processing logic 22 operates on the digitized cell images in a manner that will be her~lnafter described which includes cell feature extraction and cell classification.
A suitable stage motor means 24 is provided and controlled by stage motor electronics 26 which are in turn controlled by a master control logic 28. The stage motor 24 is provided to shift the slide 12 in order to itera-tively process different image areas of the blood speci~en on the slide. To control the focus of the microscope, a focus control motor means 30 ls connected to themicroscope and is operated by focus motor electronics 32 which are also controlled by the master control logic 28 by means of th~ focus parameter electronics 34. Focus control of slides for image analysis is well known in the art, e.g., U.S. Patent No. 3,967,110.
The apparatus 10 shown in Figure 1 includes a housing 38 having a cover 40 enclosing the microscope ;~ optics 14 and the television vidicon 16. An upper s~tion 42 of the housing 38 houses the control switches of the apparatus, the next lower section 44 houses the master control logic 28 with the next two lower portions 46 and 47 of the housing containing the memory store for the image processing logic 22 and master control logic 20 and the motor electronics 26 and 32. A terminal 48is connected to the master control logic 28 and has a keyboard 50 for input of identifying information about the specimen or for other instructions. A monitoring screen 52 provides a visual display of the final report, and preferably a written printout is also made by a printer means 54 to ~ 35 afford a permanent record. A TV monitor 55 provides 'i,~ desired pic~orial displays. The TV camera electronics are , housed in a section 49 below the monitor. The next lower ,, .

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section 51 houses the analog to digital converter with : the first section 53 housing the image processing logic 22.
The results of the red cell analysis may also be trans-mitted for storage i.n a medical computer data bank.
In accordance with the present invention, red . blood cells may be examined such that normal cells are distinguished from abnormal cells and classified by the apparatus 10 into subpopulations automatically in a detailed ; fashion heretofore not possible by a manual/visual exarnina-i- 10 tion of cells. Also, each of the red blood cells being examined may be classified into mutually exclusive sub-populations and reported out so that the presence of a ; minor number of abnormal cells is not overlooked or for-~otten and so that accurate parameters about a given sub-population may also be provided. For the first time, the individual red blood cells may be examined individually . for the hemoglobin contents. Thus, a report may be made not only of the kind of cells found in the subpopulation .; but also of their number and thelr hemoglobin characteris-.. 20 tics. ~dvantageously, the individual red blood cells may be analyzed and classified with less subjectivity into a large number of mutually exclusive subpopulations (Table I) . such as biconcave (round cells with central pallor), ` elongated cells, targets, and irregular cells (cells not - 25 fitting into any of the above classifications).
. The preferred hemoglobin characteristic gathered from the analysis of the hemoglobin contents of the indi-. vidual cells within a given subpopulation and reported out is the mean cell hemoglobin (MCH) for a given sub-: 30 population of cells, such as shown in Table I. In addition ' to the hemoglobin parameters, the individual cells are . counted for each subpopulation to provide their respective percentages of the total population; and likewise mean ~ cells area (MCA) for each subpopulation may also be reported as shown in Table I. In has been found .to be helpful in detecting subnormalities in blood samples to determine multivariate distributions of the red blood . .
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cells in particular subpopulations of a sample with respect to a plurality of quantifiable features. In this regard a bivariate distribution is shown in Figure 13a as a ; distribution of round biconcave cells, with respect to a preferred quantifiable feature cell area on one axis and the cell hemoglobin content on the other axis.
By means of a measu:rement and analysis procedure to be described, parameters a:re reported which describe this distribution with regard to lts central disposition, or mean values over the plurality of variables, and its variability, spread, or dispersion. The mean cell area and mean cell hemoglobin describe the center of the dis tribution and are reported as shown in Table I. Two other statistical parameters EVl and EV2 are reported in Table I
and describe the variance of the dispersion of the distri-` bution in the orthogonal directions of its major and minor elliptical spread. EVl and EV2 stand for eigenvalue 1 and eigen~7alue 2, respectively, and describe the dispersion or spread of the distribution. If the points of the dis~ibu-. 20 tion are thought of as defining an ellipse, then EVl and :~ EV2 can be thought of as relating to the length and breadth ; of the ellipse. Advantages derived from reporting param-eters relating to a distribution of a particular su~opula-tion will be more fully described hereinafter.
Other parameters reported in Table I include the mean pallor volume (PAL) for the biconcave and spherocyte ,! cells as well as the standard deviation for thedistribution of the biconcave and spherocyte cells with respect to central pallor volume. The pallor volume standard devia-tion (PSD) is a parameter which describes the variance of ~ the distribution of this measure over these subpopulations ,~ of cells. Another parameter reported is the skewness(SKW) which measures the skewness of the distribution of all the cells with respect to the quantifiable feature ~perimeter ;~' 35 o~ the cell) to area of the cell.
This data has been unavailable prior to this invention from any commercial instrument, or in any other ~ , ' : , " :

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fashion, such as from special research instrumentation.
The closest analogous instrument is the Coulter Counter (Coulter Company, Hialeah, Florida) which is unable to classify red blood cells into subpopulations and which - 5 reports the mean cell size and mean cell hemoglobin for the entire population of red blood cells. As seen in Table I, the present invention is also capable ofreporting the total population, or average mean cell hemoglobin as well as the average mean cell area (which is related to the mean cell size) in addition to the other parameters suggested. In that table these are denoted in the line with AVERAGE parameters.
Thus, as indicated above, herein the invention will be described as having the ability to classify red 15 blood cells into the several mutually exclusive subpopula-tions set forth in Table I. The subpopulations listed are the preferred subpopulations for classifying blood with respect to recognized categories of anemias but there may be other subpopulations defined. The mean cell area (MC~) is reported in microns2 with the mean cell hemoglobin(MCH) reported in picograms (pg).
The severa] subpopulations described and their associated parameters hereinafter are:

. 25 Table I
~`~ 96. 8 BICONCAVE MCA MCH
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MCA 50 0. 5% Spherocytes 47 30 i MCH 31 0. 2% Elongated 5 2 ; EVl 42 2. 3% Irregular 38 23 :. EV2 2 0.2% Targets 57 34 0.9 Normal 4.1 Megloblastic 4.2 Iron Deficient 6 . 2 Hemoglobin SS
2.5 Chronic Disease 4.8 Hemoglobin SC

3. 8 B-Thalassemia 4.9 Spherocytic '.`

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. . , : : -In accordance with another aspect of the present invention, samples of blood may be analyzed and thereby classified by "slmilarity" or "distance" measures being reported to compare said sample to recognized categories of anemic or normal bloods. In the preferred embodiment, 24 parameters are measured for the subpopulations of the sample of blood taken from the patient. Of these, 16 are used for the tested sample of blood and define a point in this 16-space. Consequently, the typical parameter values ;~ 10 for a particular anemia also define a point in the 16 parameter space. In accordance with the present invention, the distance, from the point representing the values for the sample blood taken from the patient, to each of the points representing the typical parameter values for each ` 15 of the categories of anemia, may be determined. Thus, a ` physician would be able to determine which of the cate-gories of anemia the sample of blood taken from the patient most closely resembles and could make a diagnosis from that information. Alternatively, simple decision logic could point out the most probable diagnosis. The normal-ized distance of the parameter values for a sample of blood is shown for a normal category of blood as well as the recognized categories of anemia in Table I. As seen ~, in Table I, this particular sample of blood is closest to normal since 0-.9 is less than any other distance reported and therefore the blood most closely resembles normal blood.
~; With reference now to another aspect of the present invention, a multiple parallel logic architecture has been found to provide the rapid processing necessary for efficient analyzing of cells on a slide. Thus, in the preferred embodiment, there is provided a first processing means, the master control logic 28, and a second processing means, the image processing logic 22 as shown in Figure 3. The analysis of the cells on a slide requires a sequence of operations to be performed, and since one operation often requires the results of a .~ , .;. ~ ~
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previous operation, there are provided synchronizing means for synchronizing the processors so tha-t the results necessary to perform a particular operation are available when that operation is begun.
FIGURE 3 illustrates the specific interrelation-ships between the master control logic 28 and the image processing logic 22. Because of this multiple parallel logic or architecture, the master control logic may proceed with one task or operation while the image processing logic is proceeding with another operation.
As seen in FIGURE 3, the operations carried out by the master control logic 28 are listed in the lefthand column with the operations of the image processing logic 22 in the righthand column. The master control logic, after clearing its associated accumulators~ proceeds to operation 56 in which a start signal is sent to the image ; processing logic and thereafter continues to operation 58.
The image processing logic meanwhile is waiting for the start signal (operation 60) from the master control logic.
Upon receip~ of the start signal, the image processing logic 22 proceeds to operation 62 which includes digitiz~g the image produced by the vidicon camera 16 (FIGURE 2).
Upon completion of the digitizingr the image processing logic sends a "digitizing done" signal (operation 64) to the master control logic indicating the completion of the digitizing process and proceeds to operation 66. The master control logic operation 58 is currently waiting for the "digitizing done" signal and upon its receipt proceeds to move the stage (operation 60) on which the slide rests so that a new field of cells may be imaged since the previous field has already been diyitized by the image processing logic 22. The optics 14, FIGURE 2, are pro-`~ viding an imaging means of the cells on the slide. The stage motor drive 24, and the focus motor drive 30, and 1-their associated electronics, are controlled by the master control logic 28. After moviny the stage so that a new field may be imaged, the master control logic proceeds to . ~

" ~ . ' , . ~ ' :
.,,, -.. , : .

~ ~,rl.3~ ZZ~d~

operation 70 wherein the field is focused and then proceeds to operation 72.
After transmitting the "digitizing done" signal, the image processing logic scans the digitized image for ; 5 a cell boundary poink (operation 66). If a cell boundary point is found (operation 74), the image processing logic extracts the cell's boundary and features (operation 76) and classifies the cell as to its proper subpopulation ! (operation 78).
The image processing logic then returns to operation 66 and continues scanning the image Eor another cell boundary point. The scanning, feature extraction, and cell classification operations will be described in more detail helow. If the logic section 74 determines that a new boundary point has not been located, then the image processing logic proceeds to operation 80 wherein ; the features of each cell located as well as each cell's ;Zi subpopulation classification i- transmitted to the master ï control logic which will be in the process of executing operations 68, 70, or 72. The transmittal of the informa-tion is on an interrupt basis, i.e., should the master '~Z control logic be in the process of controlling the imaging means (operations 68 or 70), the master control logic will interrupt these operations and store the information Z 25 received from the image processing logic before proceeding with moving the stage and focusing the microscope. However, if these operations have already been completed then the master control logic proceeds to operation 72 wherein the master control logic waits for the data to be transmitted .i 30 from the image processing logic. In response to the receipt of the data, the master control logic will trans-mit an acknowledge signal (operation 82) to the image processing logic and then proceeds to operation 84 wherein the subpopulation data for each subpopulation is updated, ;Z 35 as will be more fully explained below.
Upon receipt of the acknowledge signal, the image processing logic proceeds to digitize the image o~
,i , .! ` :.`
'. I

3~ ,7~ 4~

the new field that has been moved into view by the master control logic. The master control logic, upon completing the update of the subpopulation data, determines at logic ; section 88 whether N, the total number of cells processed, is e~ual to 1000. If 1000 cells have not been processed, the master control logic returns to operation 58 and waits for the "digitizing done" signal from the image processing logic, otherwise the master control loyic calculates the su~population parameters (operation 90), proceeds with an ~; 10 anemia classification (operation 100), and prints the results (operation 102), as will also be more fully explained below.
Thus, because of the dual processor architecture, - the master control logic is free to control the imaging means wherein a new field is brought into view to be i~ayed while the image processing logic is proceeding with the digitizing and analyzing of the image from the previous field. Similarly, while the master control logic is accumulating the data extracted from the image by the image processing logic, the image processing logic may simultaneously digitize and analyze a new image provided by the new field which had been brought into view by the master control logic. It should be noted that although for purposes of illustration only one image processing , 25 logic is described as associated with the master control logic, it is capable of utilizing information from severaL
image processing logics operating in parallel and inde-pendently on different images.
The present invention is directed to the opti-mization of the time of analysis as well as the number of ~eatures used in the classification logic so that the amount of storage and classifying techniques may be reduced ~-l substantial:Ly along-with equipment requirements therefor.
` With an opt:imization of analysis time for classification, there is a danger that the reliability and accuracy of the classification are compromised. Despite this, a i relative].y foolproof feature set and classification logic : `' ~ ~ . . ..

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has been invented for a large number of subpopulations such as those shown in Table I. The preferred classifi-cation features are size, hernoglobin content, spicularity, roundness, elongation, central peak height (if present) ~rom cross-sectional cell scans, and central pallor. By suitable combinations and analyses of such features, it i5 possible to dif~erentiate from normal blood and to identify hiconcave round cells, spherocytes, target cells, irregular-shaped cells, and elongated cells.
In the preferred method and apparatus, the cell classifications are achieved by an image processing and pattern recognition with great accuracy and reliability by renderin~ white blood cells and other artifacts sub-istantially invisible to the optics 14 by using a light having an optical wavelength of about 415 Nanometers. At this optical wavelength, the red blood cells are relatively contrast enhanced to the ultraviolet sensitive Vidicon camera without staining, while the white blood cells and !1, ' other -formed elements are substantially invisible. The staining of the red blood cells prior to being analy~ed by a microscopic image processing technique has been found to be a time-consuming process, as well as undesirable in that the staining may introduce a number of stainea arti-- facts which detract from the accuracy of the analysis.
Fur-thermore, many of the stains are not stoichiometric in - the representation of hemoglobin concentration according to density, thus distorting the quantization of the hemo~
globin ~ontent of the cell on a per-cell basis. A parti-cular manner of vapor fixing of cells before they dry without staining thereof to prevent the ~ormation of arti-facts by distortion of the central pallor is disclosea in my co-pending Canadian application Serial N~er 330,583 filed June 26, 1979 enti~led "Method and Apparatus for the Preparation of Blood ~, ~les for Automated Analysis". ~hus, by rapidly preparing the spec~s in a monolayer~and fixingFwi-th a formaldehyde vapor prior to the drying of red blood cells,-, :

as disclosed in the aforementioned co-pending patent application, and by not employing a time consuming ; staining to contrast enhance the cells, as in white blood cell analysis, these specimens may be quickly prepared and analyzed accurately.
The location of the cell image and the identi-fication and feature extraction has been greatlysimplified as described below to locate and define the cells by a boundary procedure which defines the cell in the form of an octal chain code. The use of octal chain codes as an ; image processing technique is described in a paper by H. Freeman, "Computer Processing of Line-Drawing Images", ACM Computing Surveys 6:57, 1974. As will be explained in greater detail, the octal chain code allows feature ` 15 extraction as to: (1) cell size, (2) perimeter length and roundness shape measure, (3) irregular shape measure, and

(4) elongation shape measure. This is followed by extrac-ting the summed density or hemoglobin feature, and then by extracting cross-sectional scans (thickness/density profiles) for central pallor measurement and target cell measurement. Finally, inner central pallor bowldaries are determined and features analyzed for more precise target cell identification.
After having eY~tracted these identifying features, the cells are then categorized by a classification means. The preferred classification means (FIGURES 5a, 5b, and 5c) comprise either a digital logic system of ; electrical devices or a programmed microprocessor which uses Boolean logic to classify the red blood cells.
Referring now in greater detail to the specific features of the illustrated embodiment of the invention, l the images of the cells are digitized (operation 62 of ; FIGURE 3) in a manner known to the art, e.g., U.S. Patent No. 3,883,852 as a television digitizing system. Magni-fied blood cell images are obtained by using microscope ` optics with ultraviolet illumination, arranged to provide a 0.23 pixel resolution in the image plane. A pixel is a ' , , `' ~ , , . -. .

picture element having a specific location in the digi-ti2ed image stored in the memory analyzer.
Referring now to FIGURE 4 which illustrates in greater detail -the operation 66 (FIGURE 3) by the image processing logic, an original microscopic image which had been digitized is stored as represented by the image 108 for the purpose of further analysis. This analysis is carried out by the image processing logic and is repre-sented by the blocks indicated at 115 which comprise the operations 76 and 78 (FIGURE 3). In this preferred embodi-ment of the invention, individual cells 110 and 112 in a digitized image 108 are located by a technique in which a raster scan is made of the digitized image to locate objects above a critical threshold, such as illustrated for cell 110 in block 113. The boundary of the cell is traced by examining the neighboring pixel elements by a counterclockwise search, by techniques which are well known in the art. One such technique is disclosed in .S. Patent No. 3,315,229~ During this counterclockwise boundary tracing operation herein, the picture element at the "top" of the cell, pixel 114a, which is usually the pixel located first, and the one at the "bottom" of the cell, here pixel 114f, are stored for reference in the later analysis. The analysis process then proceeds to extract features and to classify the located cell into one of a plurality of subpopulations, as in block 115, and as described in detail later.
r,l The raster scan of the digitized image is then ~`~ continued from the bottom pixel 114f to hit the next 30 digitized cell 112 by impacting a pixel 112a which is abo~e the threshold as seen in block 116. ~fter the boundary is traced and the features for this cell are extxacted and the cell is classified, the raster scan continues from the bottom pixel 112b, and, as seen in in block 11~, no more cells are located in the image field.
` At this time, the image processing logic transmits the cell features and subpopulation classifications to the , master control logic ~operation 80) as s~own in FIGURE 4.
The initial image processing done by the image processing logic outlined in PIGURE 3 is shown in greater detail in FIGURE 5a. After the image has been digitized (operation 62), the image is scanned to locate ~ cell ~operation 66) and the boundary is traced as explained above.
Durin~ thig boundar~ tracing operation, octal chain codes are formed in an operation 119. The outer boundaries, defining a cell, are processed in the ~ollow-ing manner. Each pixel element defining the boundary is stored in a list as a series of numbers indicating a line description of the cell. For instance, referring to FIGURE 7, a digital image of cells as defined by their boundary pi~els 120 are illustrated.
As is well known in the art, e.g., as described ' in "Bacus, J. W. and J. H. Weens, 'An Automated Method of Different;al Red Blood Cell Classification with Applica-tion to the Diagnosis of Anemia', Journal of Histochem-1 20 is-try ana Cvtochemistry, 25:7, 1977", a plurality of features Fl-F4 can be computed from this chain code. The details of this computation are fully described in the aforementioned publication.
, ~he above eatures are combined with other features for use in the classification of the cells. In this regard, the followin~ features are used herein:

' .;
. .

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Table II
-Feature Description How Determined .i. Fl Area size Number of pixels enclosed by cell boundary F2 Shape (circularity) (Number2o perimeter pixels) /area F3 Shape (spicularity) Number of "spicules" on boundary ; F4 Shape (elongation) Comparison of orthogonal ; boundary chain code orientations F5 Grey levels Sum of grey levels as a measure of Cell Hemo-globin F6 Pallor (volume) The percentage volume of the central pallor '`. F7 Central peak The height of the central ; peak of a 3-peaked profile of a cell F8 Pallor (depth) For a 2-peaked profile, Y the difference of the valley from the peak ~ heights :~ F9 Pallor (circularity) (Number2of pallor boundary pixels) /area of pallor As indicated above, features Fl-F4 are calcu-lated in an operation 124 by the image processing logic as shown in FIGURE 5a. Feature Fl rela-tes to the area or size of the cell as determined by the number of picture ~ 25 elements or pixels that are enclosed ~y the cell boundary.
j Feature F2 is the (boundary perimeter)2/area and is of assistance in classifying round and non-round objects.
A round object would have a theoretical value of 4~ and non-round objects have greater values.
In actual practice the value of the perimeter squared divided by the area for round digitized objects varies as a function of the number of pixels, and in addition always involves quantization error, such that , in practice for quantized circles the value approximated 14.0, and is a better approximation to this reference number as the number of pixels, or size, of an object , :
:' increases. For total cell areas above 500 pixels, the quantization error is within +.2 units.
Features F3 and F4 relate to the spicularity and elongation shapes, respectively, F3 being a count of ! 5 the number of spicules in a chain code boundary, and F4 measuring the non-roundness due to elongation of the boundary, as shown in FIGURE 7. Feature F5 is the integrated optical density of the cell (operatlon 13G).
It is the sum of the gre~ levels within the enclosed boundaries of the cell. Feature F6, which is a measure of the pallor volume, assists in distinguishing cells with large pallors, such as hypochromic cells from nor~o-cytes. Feature F7 is equal to the larger of the two central peaks of two cross-sectional orthogonal 3-peaked thickness/density profiles, either having a central peak, and is used to detect target cells. Feature F8 is a measure of the depth of the central pallor, as determined from two cross-sectional, orthogonal, 2-peaked thickness/
density profiles. Feature F9 is a measure of the degree of roundness of the pallor itself, and is also used in distinguishing target cells.
The logic decisions for determining the various features that have been briefly described are carried out by the image processing logic using the logic flow chart shown in FIGURES 5a, 5b, and 5c. The logic decisions are made using the various features together with threshold values that are identified as Tl through Tll. The thresh-olds Tl-Tll are described in Table V and specific values are also provided. As shown therein, the thresholds are used by the logic with the various features in making logic decisions leading to the classification of the cell of interest in accordance with the flow chart shown in FIGURES 5a, 5b, and 5c. In this regard, FIGURES 5a, 5b, and 5c illustrate various decisions that are made on the basis of various features either exceeding or being less than cextain threshold values as will be specifically described.

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Referring to FIGURE 5a, an object that is loeated is examined by logic seetion 138 to determine if it is sufficiently large to be a eell, rather than a noise or dirt artifact, and thus is to be further ana-

5 lyzed. If feature Fl, whieh is the size or area of the object under eonsideration, is less than the threshold , value Tl which may be a value of about 6 microns2, then the object is not considered by the decision logic and another objeet will be loeated for analysis and elassi~
10 fieation. However, if the area of the eell is greater than the threshold value Tl, feature F5 is computed in ; operation 136 wherein the hemoglobin content of the cell , is determined. This is simply a summing of the grey levels inside the boundary of the chain eoded cell and 15 then dividing by a conversion factor 1290 or thereabout to convert the grey level measurements to pieograms of hemoglobin per cell.
, For this purpose the eleetronics generating the television signal and digitizing said signal should be 20 ad~usted to produce grey levels eorresponding to the ~ following optical density at 418 nanometers:
? Table III
Optieal Density Grey Level .134 17 ~ ,294 35 ;~ .403 52 .505 43 .605 57 Also, for ealeulation of hemoglobin and the area, the opties and television eleetronics should be adjusted sueh that round objeets of the following dimensions produce the given number or pixels.

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:

: Table IV
: ~, !' Size ~ Pi.xels ` 5 58 967 ::: 34 567 ^ 23 383 : 17 283 : 4 67 : The decision logic then operates to determine whether the cell is round or non-round. This isperformed by a logic section indicated generally at 140. The logic section 140 is shown in FIGURE 8 to include logic sub-~ sections 142, 144, and 146. The subsections 142, 144, ,: and 146 are operable to jointly make the roundness deter~
~ 15 mination with the features F2, F3, and F4 being exarnined .: with respect to thresholds T4, T5, and T6. If the cell has a small roundness value, a small spiculated value, ~; and a small elongated value, then it is considered to be round and is passed on to the next operation 148 (FIGURE
5a) which is the first step in the target cell analysisand central pallor analysis. Similarly, if it is deter-mined that the cell is not round, then logic subsection 150 (FIGURE 5a) operates to determine if the size of the cell exceeds an upper boundary threshold T2, andif it does, . 25 the cell is not further analyzed and a new cell will be considered. The effect of the subsection 150 is to eli~i-.i ~ nate double cells such as that shown in the pictorial :~ representation 152. It should be appreciated from the pictorial representation that such a double cell would :i 30 not pass the roundness test, but it is also not a non-round cell of the type for cells of classes 3 and 4.
~, Thus, it cannot be accurately classified and it is for ~i: this reason that the subsection 150 eliminates such cells from further consideration.
As previously mentioned, the roundness of the ; cell is determined by fea~ure F2 which will have a value ` of 14.0 for a perfect circle and will increase as the ., .

`~, ~ ~5~ 3 shape of the cell departs from circular. Thus, the threshol~ value T~ is chosen to reflect reasonably good circularity and if the feature F2 exceeds the threshold T4, that is an indication that the shape is not circular, ; ~ hence the logical flow to su~section 150 indicating that the obiect is not round. If feature F2 is not greater than threshold T2, it is one indication that the cell is round and if the decision from the subsections 144 and 146 also indicate adequate roundness, the logic flow then proceeds to logic subsection 148 (FIGURE 5a).
In operation 148 thickness/density profiles are extracted from the cell image. These profiles are illus~
trated in FIG~RES 9a 9c and lOa-lOc. A thickness density ~ profile is determined by the grey levels of the pixels ;~! 15 along a particular direction across the cell image. As noted earlier, the grey level of a pixel is dete~mined by the hemoglobin density at that point. It has been found that the grey level of the cell at a particular point is related to the hemoglobin density and the cell thickness at that point. Two such thickness/density pro~iles, profile a and profile b, are shown in FIGURE 9a for a biconcave cell determined in two orthogonal or transverse directions, a and b. Two profiles each are also illus-trated in FIGURES 9b and 9c for a target cell and a spherocyte cell. As seen in FIGURE 9b, one direction (direction a) practically missed the center area. Since these profiles are used to distinguish target cells (feature F7), two transverse directions are preferably analyzed. Thus for each cell, two cross-sectional profiles are determined wherein the profile relates to the thickness of the cell along the points of the cross sections.
A profile for each cell of FIGURE 9 ls discussed more fully in connection with FIGURES lOa-lOc. As seen in FIGURE lOa, the profile has two "peaks", Pl and P2, and one "valley", Vl. Pl and P2 are relative maxima of the profile of the cell with respect to the cell thickness and thus determine the two relative maximum thickness ~!

. ~
density points along the profile. V1 determines the relative minimum point oE thickness density. Similarly, ~ the target cells have three relative maxima, P1, P2, and ~~ P3, with two relative minima, Vl and V2, as shown in FIGURE lOb. The spherocyte has one peak, P1, and no valleys ~FIGURE lOc). Thes~ profiles are utilized in a ` target cell analysis and a central pallor analysis as will be more fully explained hereinafter.
`~ After the image processing logic extracts the ` 10 thickness/density profiles for the cell, it proceeds to `~ the target cell analysis performed by the logic section, referred to generally at 156 of FIGURE 5b. The first .: step of the target cell analysis is to smooth the two `~ profiles, profile a and profile b, as shown in operations 15 156 and 158, which is performed by the image processing `~ logic before proceeding to a logic subsection 160. The . logic subsection 160 determines whether a profile has . three peaks and if so forwards it to an operation 162 which determines half the average of the two non-center : 20 peaks, Pl and P3, or l'LEVl". A logic subsection 164 determines whether the two valleys, Vla and V2a, are less ` than LEVl and if so then the cell located might be a target cell and the image processing logic proceeds to examine profile b. If not, then the valleys are not deep enough in profile a to be a target cell, so the center peak, P2a, is set to zero in an operation 166 and profile ~` a is smoothed to two peaks or less in an operation 168 After profile a is examined, profile b is examined for three peaks in a logic subsection 170. If .` 30 the logic subsection determines that profile b has three peaks, it is forwarded to an operation 172.and logic sub-section 174 wherein the two valleys, V2a and V2b, are compared to LEV2 which is half the average of the two . non-center peaks Plb and P3b as for profile a. If the .~ 35 two va~leys are less than LEV2, then it is forwarded to operation 176 wherein the feature F7 is determi.ned as to which is the larger of the two center peaks, P2a and P2b, "

s of the profiles a and b. Feature F7 is compared to a threshold T7 in a logic subsection 178, and if larger, the cell is classified as a tc~rget cell ~C5). In other words, if the larger of the two center peaks is larger than a certain threshold, then the cell is determined to be a target cell~ If not, then the center peaks of the profiles are probably due to "noise" in the image video and digitizing and not due to a center area of a target ` cell. In that case, both profiles are smoothed to two 10 peaks or less in operations 180 and 183. However, if the logic subsection 174 determined that the valleys of profile b were not less than LEV2, then the profile b is forwarded to a logic subsection 184 which checks whether ; the center peak of pro~ile a had been set to zero. If not, then profile a may have detected a target cell and thus P2b is set to zero and subsection 176 determines the maximum value for F7 as described.
If the center peak, P2a, had been set to zero, then neither profile has passed the tests at logic sub-20 section 164 and 174 respectively. Thus the cell is probably not a target cell and profile b is also smoothed j to two peaks or less at operation 182. However, some target cells migh-t not be detected in this analysis, therefore, other tests are performed on the cell as will be explained later.
After the center peaks of profiles a and b have been examined as explained above, a logic subsection 186 determines whether profile a has only one peak. If so, the variables Pla, P2a, and Vla are set equal to each other in an operation 188. In either case, the imaye processing logic then examines profile b to determine whether it has only one peak, at the logic subsectionl90.
If proile b has only one peak, then the variables Plb, P2b, and Vlb are set equal to each other in an operation 192.
Continuing with FIGURE 5c therein, a feature F-8, which is the averaye value o~ the two valleys sub-:

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tracted from the average value of the four peaks of the two profiles of the cell,is determined by subs~ction 194.
Then the cell feature Fl is e~amined to determine whether the si~e of the cell is larger than a threshold T8 at a logic subsection 196.
If the cell is large, i.e., Fl is greater than T8, it is possible that the cell is a target cell despite the previous target cell analysis and therefore another target cell a~alysis will be performed beginning in operation 19~. Therein, a variable LEV3 is set equal to one-half the value of feature F8 (operation 198).
Next, a search for the central pallor of the cell is initiated by searching a direction along the line from the top pixel of the cell through the center of the cell looking for a threshold condition, i.e., hitting a pixel which is below the threshold LEV3, before the center is reached. The chain code is then formed for the central pallor boundary (operation 202). The pallor circularity feature F9 is then computed in an operation 204. F9 is calculated as the number of pallor boundar~ pixels squared divided by the area of the central pallor. F9 is then compared to a threshold value T9 at a logic subsection 206 to determine the circularity of the central pallor.
This operation is necessary since the two profiles from the previous target cell analysis may have missed the central area as shown for the cell 208. Thus, if circu-' larity feature F9 is greater than the threshold T9, then the cell is a target cell, otherwise the cell is fol~rded to the operation 209 wherein a feature relating to the size of the central pallor of the cell is computed.
The central pallor feature is defined as thepercentage volume of a cylinder, with the height and area of the cell under consideration, not occupied byhe~oglobin.
This is illustrated in FIGURE 6, where T represents the '35 cell height or thickness, and 132 indicates the indented central pallor region. The cell area is known from pre-vious analysis on that cell, i.e., Fl. Also, feature FS

. -- . . . . .

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!~ - 34 -is the sum of the grey levels for pixels enclosed by the chain code definlng the boundary of the cell. ~s noted above, the hemoglobin density is related to the thickness of the cell and in this manner the hemoglobin feature F5 defines a volume which is related to the thickness or volume of the cell. The cylinder height, or thickness (T), is derived by using the average value of the peaks of the two thickness/density profiles of the cell, as:
T = Pla = P2a ~- Plb ~ P2b Thus, the volume of the central pallor may be calculated as: T times the area of the cell (Fl) minus the hemoglobin content. Finally, the percentage pallor volume F6 is:
F6 = ~T x Fl - FS) x 100%
T x F1 ~ fter this feature has been computed, the image processing logic proceeds to a logic subsection 210 wherein ` the cell is distinguished between biconcave cells (Cl) and spherocyte cells (C2) as it has already been determined that the cell is not an elongated cell (C3), an irregular ; cell (C4), or a target cell (C5). The logic subsection 210 compares the percentage pallor volume feature F6 to a .. , ; threshold value TlO and the pallor depth feature F8 to a threshold Tll and if either feature is less than its asso-ciated threshold then the cell is deemed a spherocyte cell ;l (C2), otherwise it is a biconcave cell (Cl).
~ Referring back to FIGURE 3, the feature extrac-./ tion operation 76 and the cell subpopulation classification operation 78 have been completed for the cell that had been located in the image scan. The image processing logic will then continue scanning the image for another cell (operation 66) and if no other cells are found then the features for those cells located as well as the cells' subpopulation classifications will be sent to the master control logic in the operation 80.

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While the determination of the various features and decisions contained in the logic diagram of FIGURES
5a, 5b, and 5c is carried out utilizing the threshold values contained in Table V, it should be understood that the threshold values are based upon empirical and statistical analysis and can be varied somewhat without appreciably affecting the eventual classification of the cells. It should also be appreciated that the threshold vaIues are believed to be optimum values which have been fixed to maximize the accuracy of the classification.
Table V
Threshold Val e Description Tl 6~ Size threshold for 2 artifact T2 54~ Size threshold for double cells T3 25 Elongation threshold T4 16 Cell circularitythreshold T5 7 Spiculed threshold T6 ~5 Elongation threshold T75 grey levels Target center peak 2 height threshold T8 47~ Size threshold for target cells T9 20 Pallor circularity threshold T10 ll~ Pallor volume threshold Tll8 grey levels Depth of pallor threshold Z5 Upon completion of the feature extraction and cell classification analyses for the cells located in the image, these features are transmitted to the master control logic as illustrated in FIGURE 3. After acknowledging the receipt of the data (operation 82), the master control logic proceeds to update subpopulation measurements for each cell class located in the image just analyzed (operation 84). A diagram illustrating the updating operation in greater detail is shown in FIG~RE ll. A
plurality of accumulators are provided to produce a ~ ning total of a plurality of measurements for the cell sub-populations or classes. Each accumulation is a function of one or more cell features, such as the cell feature ` ~

-, - ;- . .. . .. ... . .. . . .

:
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.
; - 36 -value itself or the value squared, for example. The cell feature values Fl, F2, F4, F5, and F6 for a particular cell are provided as inputs to the accumulators together with the cell classification Ci to which the cell features pertain. After the measurements for the cell have been accumulated, then the other cells in the image are similarly processed to further accumulate the measurements based on all of the cell's features.
Thus, the feature F2 (cell circularity feature) is provided at a line 212 to an accumulator 214. The accumulator 214 produces a running total Sl, i.e., accumu lates the measurement (F2 - 14.1)3 for all the cells located by the image processing in logic wherein F2 is the cell circularity feature (Table IV). This measurement is used in a later calculation which provides a parameter describing the skewness of the distribution of all the red blood cells located with respect to the circularity feature of th~ cells.
; Also accumulated is the elongation feature F4 which is provided at a line 216 to accumulators 218 and 220. The accumulator 218 sums the total (S2) of the feature F4 for aIl the cells which is used to calculate the average elongation for the cells. The accumulator 220 , provides a sum or running total (S3) of the elongation '~ 25 feature F4 squared, i.e., (F4) , which is used to calculate a parameter describing dispersion, or variation of the distribution of the red blood cells with respect to the mean of the elongation feature F4.
Not all feature measurements are accumulated for each subpopulation. For example, the feature F6 (pallor volume) is only accumulated for the biconcave cells ~sub-population Cl) and the spherocyte cells (subpopulation C2). Therefor~, in addition to the features for a parti-cular cell, the subpopulation classification for the particular cell to which the features pertain is provided which is shown as Ci at line 222. A plurality of logic utilize the input Ci to discriminate among the cell sub-. ~ .
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populations. Thus, the cell classification Ci is provided to the inputs of a logic AND gate 224 and an AND yate 226 with subpopulation Cl constant (i.e., a 1) provided to the other input of the AND gate 224 and subpopulation C2 constant (i.e., a 2) provided to the other input of AND
gate 226. The output of these AND ~ates are provided to an OR gate 228 which may enab:le the accumulators 230 and 232. The accumulator 230 provides a summation of the feature F6 (central pallor vo:Lume) as indicated by input lines 242, but only when enab:led by the logic OR yate 228.
Similarly, the accumulator 232 accumulates the sum of the feature (F6)2 but only when enabled. Thus, the yates 224, 226, and 228 permit the accumulators 230 and 232 to accumu-late the measurements derived from the feature F6 only ; 15 when the feature had been extracted from a Cl or C2 bicon-cave or spherocyte class cell. The output of the accumu-lator 232 is provided at S5 which is used to compute the dispersion parameter of the distribution of spherocyte and biconcave cells with respect to the mean ~olume of the central pallor of the cells. The output of the accu-mulator 230 is provided at S4 which is also used to calculate the dispersion parameter and also to calculate the mean or averaye central pallor volume for the sphero-cyte and biconcave cells.
' 25 Similarly, a logic AND gate 234 enables accumu-lators 236, 238, and 240 when Ci at line 222 is equal to a 2, i.e., the cell features appearing on the feature lines 244 and 246 were extracted from a class C2 (sphero-cyte) cell. The accumulator 236 accumulates the feature Fl (cell area) which is provided at Sll, which will be used to calculate the mean cell area parameter for the ;~
cells in the C2 classification. The accumulator 238 provides at S12 the accumulated total of feature F5 (cell h~moglobin content) which is used to calculate the mean ceIl hemoglobin content ~or the class C2. The accumulator 240 provides a total of the number of cells in the C2 class, i.e., N2 equals the number of spherocyte cells .

~3~

located by the image processing logic.
In a similar manner the total cell area for the elongated (C3), the irregular (C4), and target (C5) cells are provided at S13, S15, and S17, respectively. The 5 total of all cells' hemoglobin content for the elongated, irregular, and target cells is provided at S14, S16, and S18, respectively. The total number of cells in each of the above subpopulations is provided at N3, N~, and N5.
Likewise, the total of all of the cells' areas 10 for the biconcave subpopulation is provided at S6, the total of all the cells' hemoglobin contents is provided at S7, and the total number of biconcave cells is provided at Nl. For additional accumulated measurements on the bi-concave subpopulation, additional logic gates permit 15 accumulators to discriminate among the class cells. Thus, an AND gate 248 enables accumulators 250, 252, and 254 when the features appearing at the lines 244 and 246 have been extracted from a Cl, i.e., a biconcave cell. The accumulator 250 provides the accumulated sum of the measure-20 m~nt (Fl) at S8. The accumulator 252 similarly provides the accumulated total of the measurement (F5)2 at S9.
Finally, the accumulator 254 provides the accumulated sum of the product of the feature Fl times the feature E5 (Fl ~ F5). The accumulated S9 and S10 are used to calculate 25 parameters descriptive of the dispersion, or variation of the bivariate distribution which will be further explained L hereinafter.
~hus the features for each cell examined by the image processing logic provide the inputs to the logic 30 described in FIGURE 11 for updating or accumulating measurements based upon the cell fPatures with the parti-cular measurements updated for each cell depending upon the subpopulation classification to ~hich that particular cell belongs. The measurements updated ~y the logic of 35 FIGURE 11 provide an intermediate step for the calcu~ation o~ parameters which are descriptive of each subpopulation classification as well as parameters which ~re descriptive `:

- : , ~ , , ~ ;; . , , i ',' ' ! ' , ,~ . . ' ' , .

~,f~

of multivariate distxibutions of cell subpopulations with respect to different cell features.
Referring back to FIGURE 3, it is seen that at logic subsection 88 the determination is made whether a preset total of N cells have been processed. If not, the master control logic returns to operation 58 wherein it waits for the "digitizing done" signal indicating that the image processing logic has completed digitizing the next field. If N cells have been processed, e.g., N=one thousand, then the accumulated measurements which had been updated as illustrated in FIGURE 11 for those N cells are used to calculate the parameters descriptive of the sub-populations (operation 90) which is illustrated in grea-ter detail in FIGURES 12a through 12e.
The output Sl of the accumulatox 214 (FIGURE 11) is used in the calculation of a dispexsion parameter which describes the skewness of a distribution. Herein, a dis-tribution of all the cells with respect to the elongation feature (F4). Skewness is calculated as - 20 S~ [ - ~ 1 Thus a logic subsection 256 having inputs S2 and N produces the skewness parameter:

S~
' The calculation of the skewness parameter is quite helpful in describing a population of cells. For example, a distribution of normal cells is shown in FIGURE 15, generally at 255. The distribution is with respect *o feature F2 (circularity). Also shown is a dis-tribution of sickle cell anemia cells, generally at 257.
As can be seen there, the distribution of sickle cells is greatly skewed toward the right, indicating a great number of elongated cells. Note, however, that the mode of both distributions is identical. Thus, the skewness parameter . ~. . , ~t~

is a valuable comparison tool for indicating anemias.
A logic subsection 258 having inputs S2 (the sum of the elongation measurements for the cells) and N
(the total number of cells) produces the mean cell elonga-tion parameter (ELN).
The general formula for the dispersion in the form of the standard deviation of a distribution with respect to a variable X is given by:

~ X~
~d~ ~e~ ~
, A logic subsection 260 produces the standard deviation of the elongation distribution of cells with `~ respect to the elongation features. The logic subsection 260 has an input S2 equal to E Fq~ (FIGURE 11) N
~; and an input S3 equal to ~ and produces the elongation standard deviation (ESD) after the square root of the output has been taken by a logic subsection 262.
A parameter for the mean central pallor volume (PAL) of the biconcave and spherocyte cells is provided by a logic subsection 264 having inputs Nl (the number of , biconcave cells), N2 (the number of spherocyte cells), and S4 (the accumulated sum of the volumes of the central pallors of those subclassifications). A parameter of the distribution of the biconcave and spherocyte cells with .. .
respect to the central pallor volume, herein, the central pallox volume standard deviation (PSD) is provided by a logic subsection 266 having inputs S4 and S5 and a logic subsection 268 which takes the square root of the output provided by the logic subsection 266 to finally produce the parameter PSD in a manner similar to that of the ., .
~; :

parameter ESD.
A distribution of three different populations of cells, normal, spherocytic, and iron deficient, with respect to the feature F6, the percentage volume of central pallor is shown in FIGURE 16. It is important to note that the distribution of normal cells at 267 has the same mean value (PAL) as the distribution cf iron deficient cells at 269, yet they have a different variation or standard deviation (PSD) in central pallor volume. On ; 10 the other hand the distribution of normal cells has the same standard deviation as the distribution of sphe~cyte cells at 271 but a different mean value. Thus both paramr eters have been found advantageous in the classification of blood with respect to anemias.
Two other parameters, EVl and EV2, are computed utilizing the accumulated sums S6-S10 and Nl and which are descriptive of the amount of dispersions of a bivariate distribution of the biconcave cells. The two variables of the bivariate distribu~ion are the cell size and the cell hemoglobin content. Four such distributions are illustrated in FIGURES 13a-13d wherein the cell area defines the abscissa axis and the cell hemoglobin content def:ines the ordinate axis. Each "X" represents a biconcave cell with its location within the graph defining the cell's area and hemoglobin content. Thus, as can be seen in the four FIGURES, the cells are distributed mainly on a 45 line passing through the origin. The mean cell area (MCA) and the mean cell hemoglobin (MCH) define the center of each distribution. The values, EVl and EV2, define the dispersion or the amount of spread of the distribution in ~ two principal independent axes. In particular, EVl ! describes the amount of spread of the c].uster or distribu-tion along the direction at essentially 45, or along the ; line of ma~or dispersion of the ellipse with EV2 describing ; 35 the dispersion in a direction which is ~rthogonal or trans-~¦; verse, that is, 90, relative to the dispersion of EVl.
Referring to FIGURE 12a, a l~ic diagram is .
~' .: .. . . - l i ,.. :: ., ,, , : -, 3~ 3i'd~

~ 42 -shown for the computation of the parameters EVl and EV2.
The general formula for computing the variance of a dis-tribution with respect to a variable is similar to that given for the standard deviation. The variance of the distribution with respect to cell area is provided by a ; logic section 270 which has inputs N (the number of bi-concave cells~, S8 (the summation of (Fl)2 for each bi-concave cell~, and S6 (the summation of Fl for each bi-'~ concave cell). The variance of the distribution with respect to hemoglobin content is provided by a logic sec-tion 272 which has inputs Nl, S9 (the summation of (F5)2), and S7 (the summation of (F5)). A logic section 274 provides the sum K of the output of the logic sections 270 and 272 and a logic section 276 provides the product A of the output of the logic sections 270 and 272.
The covariance of the distribution with respect to both the cell area and the cell hemoglobin content is provided by a logic section 278 having inputs Nl, S7, S6, and S10 (the summation of the product Fl times F5 foreach biconcave cell). A logic section 280 squares the output of the logic section 278 to produce an output B. A logic i section 282 subtracts the output A of the logic section 276 from the output B of the logic section 280 to provide an output D. K and D are coefficients of a quadratic ; 25 equation wherein a logic section 282 produces the first solution, EVl, to the quadratlc equation, and the logic section 284 produces the second solution, EV2, to the equation.
A logic section 286 produces the mean cell hemo-globin parameter for the biconcave cells by dividing thetotal hemoglobin content S7 for all the biconcave cells by the number (Nl) of the biconcave cells. The mean cell area (MCA) of the biconcave cells is produced by a logic section 288 which divides the total cell area (S6) of the biconcave cells by the total number (Nl) of the biconcave ; cells.
~n a similar manner, as shown in FIGURE 12b, ; -~l3S~

- ~3 -the mean cell area and mean cell hemoglobin parameters are computed for the remainin~ four classes or subpopu-lations, i.e., the spherocytes, elongated, irregular, and target cells by eight logic sections 2gO-297. The number of cells in each subpopulation, Nl-N5, are each trans-formed into a percentage of the total number of cells by five logic subsections 300-304, in FIGURE 12b. For example, the percentage of biconcave cells (NCl) is ` provided by logic s~lbsection 300 which divides the number of biconcave cells (Nl~ by a total number of cells located by the image processi.ny means (N) and multiplies by 100.
Finally, in the preferxed embodimentj two other parameters are calculated which describe the entire popu-lation of cells analyzed as illustrated in FIGURES 12d and 12e. First, a mean cell area parameter (MCA) is cal-culated as a weighted average by multiplying the percentage of a subpopulation (i.e., Ni-NC5 being first divided by 100) by the mean cell area for that subpopulation for each subpopulation and adding the products to produce the weighted average. For example, the percentage of biconcave cells (NCl) is multiplied by the mean cell area (MCAl) for the biconcave subpopulation by means of a logic section 306 and the percentage of the spherocyte cells (NC2) is multiplied by the mean cell area of the spherocyte cells (MCA2) by means of a logic section 308 and so on for the other subpopulations and adding these five products by means of a summation logic section 310 to produce themean cell area (MCA) for the entire population. A weighted average of the hemoglobin content for the entire popula-tion (MCH) is produced in a similar manner by a pluralityof "multiply" logic sections 312-316 and a summation lo~ic section 318.
In the above manner, 24 parameters descriptive of the various subpopulations of red blood cells and the entire population of red cells as a whole may be calcu-lated, 22 of which are listed in Table I. They are the j percenta~e of the entire population for each su~popu~tion, .~, . . .
,_ ' ~ . ' . `': '~' . , , ` ; , ,' , , ' ` :;. `' : ' : . '~ ' ~'. '''.. ;' ' ' .. ' ." ~ , . ' ' . '. ' ' '.: .. '`` , ` '' ' ', . , , : , . ;.

~3~

the mean cell area (MCA), and the mean cell hemoglobin (MCH) for each subpopulation, the MCA and MCH for the entire population, the mean central pallor volume (PAL) of the distribution of biconcave and spherocyte cells, the standard deviation (PSD) of the central pallor volume distribution, and the skewness (SKW) of the circularity distribution of the entire population. Two parameters, the mean of the elongation distribution (ELN) and the standard deviation of the elongation distribution (ESD), are calculated but in the preferred embodiment are not reported, as in ~able I. The parameters in Table I show values calculated for a sample of blood taken from a patient. Similarly, a sample of blood may be taken from another that is known to exhibit one of the known cate-gories of anemia such as iron deficiency, for example, ;; and the 16 parameters may be calculated for the known anemic sample. Subsequently, the parameters calculated from the analysis of the sample of blood taken from the patient may be compared to the parameters of an iron deficient anemic sample to determine if the sample from , the patient resembles iron deficient blood. Likewise, parameters may be calculated for a plurality of known anemic samples of blood wherein the parameters of the patient's sample may be compared and in this manner the patient's blood may be classified with respect to thoserecognized categories of anemic blood. Referring to Table I, it is seen that the sample, from which the param eters of Table I were calculated, has been compared to eight types of blood which are normal, iron deficient, chronic disease, B-thalassemia, megaloblastic, hemoglobin ss, hemoglobin SC, and spherocvtic.
A specific classification technique to produce a similarity measure for the sample blood taken from the patient which is compared to recognized categories of anemia and normal blood is shown in FIGURE 14. Sixteen of the 24 parameters can be thought of as defining a sixteen variable space or sixteen-space. Values for the ~ .' !, . ', . . ' . . ~ . . , ~ 45 -I sixteen different parameters would define a vector having sixteen components, one for each parameter~ Thus, when a sample of blood taken from a patient is analyzed, the sixteen parameters calculated therefrom would define a vector Y having sixteen components (Yl....Yi....Y16).
Similarly, analysis of samples from the eight previously ; mentioned types of blood, normal and anemic, would define eight vectors, Wi 1 to Wi 8. Each component of the vector for a category of anemic or normal blood is determined by obtaining blood with prior Xnowledge of the anemic condi-~' tion and measuring mean parameters over a plurality of such bloods for the sixteen parameters for that particular category. The vector Y representing the parameter values calculated for the sample of blood taken from the patient may he compared to the vectors representing the mean of various categories of anemic and normal blood. The vector that the vector Y most closely resembles, i.e., is the ~` closesit to in the sixteen-space, would determine the classification of the patient's blood.
~,~ 20 The first step in the anemia classification logic of FIGURE 14 is to normalize each parameter value , ~ to produce the sixteen components of the Y vector. Thus a parameter value Xl which represents the mean cell area of biconcave cells in the patient's bl~od sample is normalized by a logic subsection 320 to produce the first component of the Y vector, Yl. The logic subsection 320 in normalizing the parameter value Xl subtracts the mean value al ~rom Xl and divides by the standard deviation b of the distribution of X with respect to the biconcave cells. The distribution for each of the sixteen param-eters has been determined with the mean ai and the standard deviation bi for each of the sixteen parameters as set forth in Table VI below.
"

.j ,~; . .
.. . . .

,, , ., - ,, . , : :, :

.: Table VI
ai bi (mean) (standard deviation) parameter i = 146.677 7.103 ~CA - X
5 226.531 5.775 1 2 354.115 28.972 EVl = X3 4 3.594 2.120 EV2 = X4 520.271 3.821 PAL ~ X5

6 4.646 1.161 PSD = X6 ; 10 776.934 18.331 NCl = X7 8 4.167 8.193 NC2 = X8 : 9 3.038 5.600 NC3 = Xg 10 7.459 7.954 NC4 = X10 11 8.402 10.828 NC5 = Xll ~ ].51246.490 6.609 MCA = X12 ; 1326.490 5.548 MCH - X13 14 6.893 3.059 ELN = X14 15 5.949 2.363 ESD = X15 1612.218 4.879 SKW = X

:: 20 : There are sixteen logic sections represented by . ~: a logic section 322 which normalizes the parameter Xi to produce one of the sixteen components Yi of the vector Y.
The vector Y representing the sixteen parameter values for the sample of blood taken from the patient is compared : to the eight vectors Wi 1 to Wi 8 representing the param-eter values for each of the eight categories of blood to determine the proper classification for the patient's blood.
Accordingly there are provided eight logic : ~: sections represented by a logic section 324 having the sixteen components YL-Y16 of the vector Y as inputs. In 1.~ addition, each of these logic sections has the sixteen -~ components of a vector representing the parameter values of one of the categories of blood. For example, the first logic section 326 compares the~parameter values for the ,!
~i:

~3~

- ~7 -patient's blood with the parameter values for normal blood. In this connection, the veetor Wi 1 represents the sixteen components of the vector for normal blood.
The two veetors Y and Wi 1 are eompared by the logie seetion 326 wherein the standard distance formula is used to caleulate the distanee between the two vectors to produee a distance Dl. Referriny to Table I, it is seen that the patient's blood parameter vector has a ~ distance of 0.9 to the normal blood parameter veetor.
;~ 10 In a like manner, the patient's parameter veetor is eompared to the parameter veetors for the seven categories of anemia, as seen in Table I. The patient's blood parameter veetor has a distanee of 4.2 to the iron ~ deficient category of anemia, a distance of 2.5 to the 'I 15 ehronie disease category, and so on.
, The sixteen components of the parameter vector for eaeh of the eight eategories of anemic and normal blood are set forth in Table VII below.

. , .

.: ~
',,`:
, ., , :
,' .
, ; ' ~ .
., .

~ j :

,, ~

: .

,, .; - , , . .. , , . , : : .

~3~
Ln 1~ Ln O O ~D N r ~ ~ r ~ a~ o Ln r-u ~ r- ~ oo w Ln ~ ~ ~ ~ r. ~ In ~: ~ o~ r; Ln ~ N O Ln ~ ~O ~r O ~ r r; u L4 ~ O O O O N O O N O O O O O O O O
,, U~ U I
,.
00 ~r ~ N CO ~ Ln N ~ ~ ~r ~ N ~ O
o~ o Ln ~ N N ~ ~ ~r) ~ O ~1 r ~ r. ~
c~ r Ln ~ co ~ N C~ O r~l ~) ~I N r. N Ln N O
cn O O O O O O r-l O O O N O O O O O
~;~ I . I
: ~I N ~r ~ Ln Ln oo ~3 r- ~r o ~ r N O
. ~ ~ C5) 0 N ~ ~ Ln ~ r ~ Ln co U~ ~ t` 'I' ~) ~) ~ O O ~I N r- ~ Ln N r~ ~ ~
U~ , O O ~ ~ O ~ ~ O ~ C~ O O O ~1 ~ ~

. Ln ~ co r ~r Ln o ~ C~ ~ N ~ 00 r-l N r-l r. ~g o ~ o o N ~ ~ O co O ~ c~ ~ Ln n ~ o o o ~ o ~ Ln r~ o~ ~ N ~ d~
. a) ................
~1 ~ O ~10000000 r~ 1000 l l l l l l l .
. o~ ~ ~ r ,~ ~. o ~ ~ o r r r. r ~ ~r .~: H ~ ~ r ~ O O~ ~) r- ~;r ~ ~ o o ~ r~ ~ Ln ~ Ln ~r r ~ r~ o ~ rn Ln ~ ~ N ~1 ': ~ ~ . . . . . . . . . . . . . . .
E~ oooooooooooooc~oo ,. 1111 11 111 E~ U
~l co ~ ~ ~ Ln ~ ~ ~ ~ ~ o r!~ N W ~r O
~ '~ N ~ ~r ~ Ln ~ Ln r r. ~ ~
o ~ ~ o Ln ~ N ~) ~ r~ ~ ~1 ~ N O ~ ~ ~
5_1 ........ --------~ OOOOOOOOOOOOOOOO
I I I 1 , 1 1 1 1 .
:' L~
o ~ r N r~ O N ~ ~ ~I t') O N a~ N 0 ~1 o r~ ~ Ln ~ o Ln r. Ln ~ co co r.
~ NLn O ~ N U~ ~ ~ ~ N~1 ~) ~ O O O r-l :, O O1-i 0 0 0 0 0 0 0 0 0 0 r-i O O O
H l l l l l l l l .~

.-, ~
.~ ~ ~1 ~ o ci~ Ln cn r ~ a~ ~ Ln ~ ~ o r- r~ ~
. ~ N OD ~7 ~ O r-l r- ~ co ~ o ~ Ch o ~7 o o I~ o Ln ~D 00 0 ~ O ~ ~ r; o Ln ~
. z ~ ooooo~1ooooooooo ;.
~;
.' ~ N ~ ~r Ln ~7 r~ co cn o ~ ~ ~ ~r Ln u ':j .,,¦

,. Ut O L~l O

`~

`

- - , ~ : , : , 5~o~

Referring back to FIGURE 3, upon completion oE
the anemia classification (operation 100~, the master control logic proceeds to print the results (opexation 102) of the analysis and similarity comparison or class-ification. One example o a printout by the preferredmethod and apparatus has already been given as Table I.
The printout in Table I indicates that the sample o~
blood analyzed is closest to normal based on the features analyzed. Two more examples are given in Tables VIII and IX respectively, with Table VIII indicating hemoglobin SS
. anemia and Table IX indicating ~ thalassemia.
TWO examples of results of recl blood cell analy-sis with the present invention will be listed below in Tables VIII and IX.

Table VIII
66.5~ Biconcave MCA MCH
i MCA 51 2.3% Spherocytes 48 30 i MCH 29 7.4~ Elongated 41 24 ~' EVl 72 14.6% Irregular 42 25 ! EV2 6 9.2% Targets 58 32 Average 49 MCA 29 MCH 18 PAL5 PSD17 SKW
5.2 Normal 4.2 Megaloblastic 3.8 Iron Deficient1.6 Hemoglobin SS
; 25 4.0 Chronic Disease3.8 Hemoglobin SC
3.9 ~-Thalassemia 5.9 Spherocytic Table IX
78.2% Biconcave MCAMCH
MCA 37 2.4~ Spherocytes 31 19 MCH 21 1.0% Elongated 26 15 EVl 36 13.5% Irregular 33 19 EV2 3 4.9% Targets 39 21 Average 36 MCA 21 MCH 17 PAL 5 PSD 12 SKW
4.2 Normal 6.5 Megaloblastic ~ 1.7 Iron De~icient 5.7 ~emoglobin SS

;~, ~, ~ - . .. . . .. . . .

Lf~f~iff~ f 7~

Table IX continued ; , .
2.5 Chronic Disease 4.9 Hemoglobin SC
1.8 l3-Thalassemia 4.1 Spherocytic From the foregoing, it will be seen that the present invention allows a new and improved analysis of red blood cells heretofore not possible (even with the system disclosed in the above-identified copending appli-cation) and the discovery that the red blood cell popula-tion carries sufficient information to diagnose many anemias without resort to other conventional tests. frhat is, subtle, slight, and early changes in either cell production or destruction may now be discovered, such as ` J 15 the incipiency of an anemia because of the ability to - measure accurately the hemoglobin content in individual blood cells, the cell shape, the cell size variations in size of central pallor, as well as the count of individual ,~
cell sizes, and an understanding of the total red cell population in blood samples t.hat was heretofore not possible. With the continual process of red blood cell production and destruction over a 120-day life span for each cell, the high percentage of old normal red blood cells would mask the smaller number of new red cells being i 25 produced at the incipiency of a particular anemia. For instance, a chronic disease anemia such as caused by an '~ infection, cancer, or tuberculosis, may cause the new red blood cells being produced to be smaller in size with larger central pallors than is normal and with decreased hemoglobin content. Naturally, early detection of a chronic disease kind of anemia would be most helpful in f ~ the treatment of this particular anemia.
Wlth the present invention and for the first t.ime, a total spectrum or galaxy of cells (i.e., the red ~, 35 cell population) may be automatically analyzed on an individual cell basis and over a sufficient number of cells and with sufficient accuracy to detect a dispersion i .. .. .

' f ~3~

of distribution indicative of a paxticular kind of anemia.
Take, for example, a b]ood sample from a person suffering from an iron deficiency anemia. Typically, in such an instance, the usual values of dispersion, e.g., EVl, will be increased from a normal blood value of about 30 to values such as 45 or higher, and likewise the usual value of dispersion for EV2 will be increased from about 2 for a normal blood to about 3 or 4 or higher for an anemia such as an iron deficiency anemia. These slightly higher values of dispersion of distribution indicate that the normal cell population is changed because of the addition of these additional cells having small size, A large central pallor, and low amounts of hemoglobin. Thus, the diagnostician will see that the normal closely packed cell population has been expanded by these new cells formed a~ter the onset of the anemia. The other values, such as location of the central tendency of the population's dispersion of distribution, which location is defined by the MCA and MCH values, may also have shifted because of the new anemia cells present. It is to be understood that the parameters used here1n, such as MCA, MCH, EVl, EV2, skewness, etc., along with the central pallor d~crip-tors, have been experimentally found to be most powerful (at this time) in analysis of the anemias illustrated and described herein. With further investigation, it may be , that other parameters and/or measures may be used to ` describe and define the red cell population but such changes will still be within the purview of the appended claims and this invention. By way of example, the covari-30 ance, or correlation coefficien-ts could be used to describe the red cell population as well as other measures, which are used to define a distribution and dispersion, and which could be used in lieu of the terms used herein in ~detail to describe the population of red blood cells.
Further, the present invention has been descri~ed principally in connection with the variables of mean cell hemoglobin and mean cell size although other variables . .

: " ~ ' .' ' ' . :' ". ! ' ',. ,, ` , "', ' ' ~3,s;~

such as pallor size and mean cell area have been tried and could be used. The particular parameters used and the names thereof may also be changed from that descriked herein and still fall within the purview of the present invention and the claims here:inafter recited.
The present invention is not to be construed as being limited to the classifying of cells into mutually exclusive subpopulations prior to making analysis of the dispersion of distribution of the red blood cells. For instance, it is possible to measure the characteristics of each of the red blood cells as to size, shape, hemo-globin, central pallor, etc., and then to make a multi-` variant dispersion distribution analysis without having a separate analysis of the biconcave cells as described herein. The classifying of the cells into a biconcave subpopulation and into other well known subpopulations is done because it is thought to be helpful to the diagnos-i' tician. Also, the classifying and reporting of subpopu-lations of hiconcave cells, spherocytes, elongated cells, target cells, and irregular cells may be eliminated. The , latter has been included merely as an aid to the diagnos-tician. Moreover, the listing of a plurality of anemias t ~ could be eliminated with the analysis and report being made only for one or more specific anemias thought to be most likely for the patient, or only that the blood sample j appears to be normal. On the other hand, in a screening process of large numbers of blood samples, each from a different person, it may be more helpful to include other . anemias in addition to the seven anemias listed herein.
The invention is thought to provide a particularly power-ful tool for the screening of blood samples as well as for verification of anemias where a visual examination or other tests leave the diagnostician to suspect the presence of an anemia.
Of the seven listed anemias, the hardest anemias to distinguish from each other are the iron deficiency, -; chronic disease, and ~-Thalassemia. The accuracy of the f . . :, . , ~

~3~

diagnosis of these three anemias from one another is thought to be about 80% accurate with the existing equip-ment descri.bed herein. It is thought that the other anemias listed herein can be diagnosed with almost 100%
accuracy. Generally speaking, when it has been found that the closest anemia listed was one of the three anemias of iron deficiency, chronic disease, or ~-Thalassemia and that the closest anemia was not verified, the actual veri-. fied anemla will then be the next closest one listed of these three anemias. For this reason and other reasons,it is preferred to quantify the closeness of several anemias so that if the first anemia is not veriEied then the second closer anemia can be next chosen and examined for verification.
It will be recognized by hematologists skilled in the art that the diagnosis of some anemias, such as `~ iron deficiency or chronic disease anemia, are most diffi-cult today ~7ith conventional equipment even with all the information of other tests available to the hematologist.
The present invention should provide a very useful tool for verification of a particular anemia when the other ` conventional tests have been used and need to be verified.
~t Although the term "anemia" has been used exten-i~ sively in this description, it should be noted that the term has been used in the gen~ral sense, and the present . invention may be used in a detection of other red cell ~ disorders or pathologies such as hereditary ellipto~tosis, ; for example, or others.
Although there has been described herein the use of first and second microcomputers, it is to be understood that only one larger computer could be used, or hard wired ' logic could be used. On the other hand, more than one ; additional microcomputer may be added with each simultane-~` ously measuring characteristics of a different red blood cell and each classifying different red blood cells into subpopulations. Thus, it is considered that one or more ` additional microcomputer may be used than described herein .~

- s~ -to expedite the system.
The above-described description and drawings provide a clear understanding of the invention and an enabling disclosure to persons skilled in the art. A
specific example of the preferred equipment practicing the invention herein described is as follows: In the preferred embodiment, the master control logic 28 and the image processing logic 22 which carry out the flow diagram of FIGURE 3 comprise two microcomputers such as the Digital Equipment Corporation LSI/11 microporcessors.

, , . .
.~

~ .

..... , ~ :
. , : . . , ,; , .
- .. . .

. ~ ~ , . . . . .

Claims (50)

The embodiments of the invention in which an ex-clusive property or privilege is claimed are defined as follows:

1. A method of automatically analyzing red blood cells in a sample of a patient's blood for an anemia or other red blood cell disorder comprising the steps of:
examining the red blood cells in patient's blood sample, measuring characteristics of blood cells and classifying normal and abnormal cells into a plurality of mutually ex-clusive subpopulations, determining parameters for the red blood cells in respective ones of said subpopulations, and comparing parameters of respective ones of the patient's red blood cell subpopulations with predetermined reference characteristic values of red blood cell subpopulations from a person having a known kind of anemia or other red blood cell disorder, and reporting the results of the com-parison to provide an indication of a specific anemia or red blood cell disorder or the lack thereof.

2. A method in accordance with Claim 1 in which the step of classifying the red blood cells includes the step of classifying spherocytic cells, target cells and elongated cells into different subpopulations and in which the comparing step comprises a comparison of at least one parameter for each of the spherocytic, target and elon-gated cell subpopulations to a predetermined reference characteristic value for similar cell subpopulations of persons having recognized kinds of anemias.

3. A method in accordance with Claim 1 in which the classifying step comprises separating biconcave red blood cells having a substantially round exterior and a central pallor of a predetermined configuration into a cell subpopulation constituting the major subpopulation of cells, and in which the determining step includes generat-ing parameters indicating the variation of cell size and of cell hemoglobin for said subpopulation of cells having the biconcave cells.

4. A method in accordance with Claim 3 in-cluding the steps of generating a mean cell size para-meter, generating a mean cell hemoglobin parameter, and generating first and second eigen parameters fox said sub-population of cells having biconcave cells therein.

5. A method in accordance with Claim 1 in which the step of determining predetermined parameters in-cludes generating a parameter of dispersion of distribu-tion of cells in at least one subpopulation and determin-ing a parameter with respect to variation in size of cen-tral pallors for the red blood cells.

6. A method in accordance with Claim 5 in which the step of generating parameters comprises generat-ing a skewness parameter for said subpopulation having bi-concave cells therein.

7. A method in accordance with Claim 1 in which the reporting step comprises reporting for said cell subpopulaiton having normal cells therein values of the mean cell size, the mean cell hemoglobin, and the amount of bivariate dispersion thereof.

8. A method of automatically analyzing red blood cells in a sample of a patient's blood comprising the steps of: examining the red blood cells in a patient's blood sample, measuring characteristics of the red blood cells, determining parameters of the dispersion of the distribu-tion of the measured characteristics of said red blood cells, and reporting on said parameters to provide a des-cription of the blood.

9. A method of automatically analyzing red blood cells in accordance with Claim 8 in which the step of examining the red blood cells includes the step of segregating individual red blood cells into mutually ex-clusive subpopulations and in which the step of reporting on said parameters includes the step of reporting the para-meters of dispersion of distribution of one of said sub-populations of red blood cells.

10. A method in accordance with Claim 8 in-cluding the further step of comparing the parameters of the dispersion of distribution of the patient's blood to reference characteristic values for a specific anemia or blood disorder and reporting the results of said compari-son.

11. A method in accordance with Claim 8 in which the step of determining said parameters includes the step of determining the skewness of said distribution with respect to a measured characteristic and in which said reporting on said parameters includes reporting an indication of said skewness.

12. A method of automatically analyzing red blood cells in a sample of a patient's blood for an anemia or blood cell disorder, said method comprising the steps of: examining the red blood cells in a patient's blood sample, measuring characteristics of the red blood cells, generating a plurality of measured properties from said measured characteristics to define a patient's blood, com-paring the resemblance of the patient's blood to refer-ence characteristic values for a specific anemia or blood cell disorder, and reporting with respect to the results of said comparison.

13. A method in accordance with Claim 12 in which the step of generating a plurality of measured properties includes the step of determining parameters of dispersion of the distribution of the measured character-istics for said red blood cells, and in which the patient's dispersion of distribution is compared to reference char-acteristic values for a specific anemia or blood disorder.

14. A method of automatically and rapidly analyzing cells on a support, said method comprising the steps of: producing an optical image on an imaging means of a field of the cells on the support; converting the image into a point by point distribution representative of the image; converting each point into a digital signal;
controlling said imaging means by a first processing means so that a plurality of fields may be imaged on said imaging means; analyzing the digital signals of each field of the cells imaged by a second processing means and meas-uring characteristics of the digitized signals for prede-termined red blood cell characteristics; and reporting parameters relating to the measured characteristics of the cells analyzed.

15. A method of automatically testing blood for abnormalities by analyzing red blood cells having quan-tifiable features in a blood specimen, said method com-prising the steps of identifying at least one subpopula-tion of the red blood cells in said specimen, determining a distribution of the red blood cells for said subpopula-tion with respect to a plurality of the quantifiable features of the red blood cells; and reporting parameters relating to said distribution to provide an indication of a blood abnormality or a lack thereof.

16. A method in accordance with Claim 15 in which the step of determining a distribution comprises a determining of cell size and cell hemoglobin and the dis-tribution is a bivariate distribution with respect to the two quantifiable features of cell size and cell hemoglo-bin.

17. A method for automatically identifying target cells in a sample of red blood cells comprising:
determining a first cross-sectional profile of a red blood cell and determining a second cross-sectional profile in a direction substantially transverse to said first pro file, each of said profiles relating to the thickness of the red blood cell at points along each cross section and defining relative maxima and minima; and detecting the existence of three relative maxima and two relative minima on either profile.

18. The method of Claim 17 including the fur-ther step of comparing the two relative minima detected to a predetermiend value.

19. A method for determining a parameter relat-ing to the size of the central pallor of a round red blood cell comprising: determining a first cross-section-al profile substantially transverse to said first profile, each of said profiles relating to the thickness or den-sity of the red blood cell at points along each cross section and defining relative maxima of the profiles; de-termining the volume of a cylinder defined by the average of said maxima and the area of said round red blood cell;
and determining a volume relating to the volume occupied by the red blood cell, and determining a parameter de-fined by the difference between the volume of said cylin-der and the volume relating to the volume of the red blood cell.

20. A method of automatically analyzing red blood cells in a sample of a patient's blood comprising the steps of: examining the red blood cells in patient's blood sample, measuring a plurality of characteristics of each of the red blood cells, determining parameters of the multivariate disperison of the distribution of the meas-ured plurality of characteristics for said red blood cells, and reporting on said parameters to provide a description of the blood.

21. An apparatus for automatically analyzing red blood cells in a sample of a patient's blood for an anemia or other red blood cell disorder comprising: means for examining the red blood cells in patient's blood sam-ple, means for measuring characteristics of blood cells and for classifying normal and abnormal cells into a plur-ality of mutually exclusive subpopulations, means for de-termining parameters for the red blood cells in respective ones of said subpopulations, and means comparing para-meters of respective ones of the patient's red blood cell subpopulations with predetermined reference characteristic values of red blood cell subpopulations for a person hav-ing a known kind of anemia or other red blood cell dis-order, and means for reporting the results of the compari-son to provide an indication of a specific anemia or red blood cell disorder or the lack thereof.

22. An apparatus in accordance with Claim 21 in which said classifying means comprises means for separat-ing biconcave red blood cells having a substantially round exterior and central pallor of a predetermined con-figuration into a cell subpopulation constituting the major subpopulation of cells.

23. An apparatus in accordance with Claim 22 in which said determining means includes means for generating parameters indicating the variation of cell size and of cell hemoglobin for said subpopulation of cells having the biconcave cells.

24. An apparatus in accordance with Claim 23 in-cluding means for generating a mean cell size parameter, means for generating a mean cell homoglobin parameter, and means for generating first and second eigen parameters for said subpopulation of cells having biconcave cells there-in.

25. An apparatus in accordance with. Claim 24 in which said means for generating parameters generates a skewness parameter for said subpopulation having biconcave cells therein.

26. An apparatus in accordance with Claim 21 in which said determining means comprises means for generat-ing a plurality of parameters to define a patient's blood and in which the resemblance of the patient's blood to reference characteristic values for a specific anemia or blood cells disorder, if any, is reported.

27. An apparatus in accordance with Claim 21 in which said means for reporting reports for said cell sub-population having normal cells therein values of the mean cell size, the mean cell hemoglobin, and in the amount of bivariate dispersion thereof.

28. An apparatus for automatically analyzing red blood cells in a sample of a patient's blood compris-ing the steps of: means for examining the red blood cells in a patient's blood sample, means for measuring charac-teristics of the red blood cells, means for determining parameters of the dispersion of the distribution of the measured characteristics of said red blood cells, and means for reporting on said parameters to provide a des-cription of the blood.

29. An apparatus for automatically analyzing red blood cells in accordance with Claim 28 in which said means for examining the red blood cells includes means for segregating individual red blood cells into mutually exclusive subpopulations and said means for reporting on said parameters reports the parameters of dispersion of distribution of one of said subpopulations of red blood cells.

30. An apparatus in accordance with Claim 28 in-cluding means for comparing the parameters of the disper-tion of distribution of the patient's blood to reference characteristic values for a specific anemia or blood dis-order and for reporting the results of said comparison.

31. A method in accordance with Claim 28 in which means for determining said parameters determines the skewness of said distribution with respect to a meas-ured characteristic and in which said means for reporting on said parameters reports an indication of said skewness.

32. An apparatus in accordance with Claim 28 in which said means for measuring cell characteristics in-cludes means for measuring red cell size and red cell hemo-globin and in which said determining means includes means for determining the parameters of dispersion of distribu-tion of the red blood cells with respect to red blood cell size and hemoglobin content.

33. An apparatus in accordance with Claim 28 in which said means for measuring characteristics measures the size of the central pallors of red blood cells and in which said means for determining parameters determines the dispersion of distribution with respect to central pallor size.

34. An apparatus for automatically analyzing red blood cells in a sample of a patient's blood for an anemia or blood cell disorder, said apparatus comprising:

means for examining the red blood cells in a patient's blood sample, means for measuring characteristics of the red blood cells, means for generating a plurality of meas-ured properties from said measured characteristics to de-fine a patient's blood, means for comparing the resem-blance of the patient's blood to reference characteristic values for a specific anemia or blood cell disorder, and means for reporting with respect to the results of said comparison.

35. An apparatus in accordance with Claim 34 in which said means for generating a plurality of measured properties includes means for determining parameters of dispersion of the distribution of the measured character-istics for said red blood cells, and in which said compar-ing means compares the patient's dispersion of distribution to reference characteristic values for a specific anemia or blood disorder.

36. An apparatus in accordance with Claim 34 in which said means for measuring characteristics of the red blood cells measures the size of the central pallors of red blood cells and in which said means for generating a plurality of measured properties includes means for deter-mining the dispersion of distribution with respect to cen-tral pallor size.

37. An apparatus in accordance with Claim 34 in which said means for generating a plurality of measured properties includes means for determining the dispersion of distribution with respect to a measured characteristic of the red blood cells and for determining the skewness of said distribution.

38. An apparatus for automatically analyzing red blood cells in a sample of a patient's blood compris-ing: means for examining the red blood cells in a pa-tient's blood sample; means for measuring characteristics of the red blood cells; means for classifying the cells into a plurality of mutually exculsive subpopulations in-cluding a normal and abnormal subpopulation; means for de-termining subpopulations parameters including the disper-sion of the distribution of at least one of said subpopula-tions; and means for reporting on the results of the dis-persion of distribution as a description of the blood.

39. An apparatus in accordance with Claim 38 in which said means for determining said parameters deter-mines the skewness of the distribution with respect to a measured characteristic for al:L of the red blood cells;
and in which said reporting means reports the indication of said skewness.

40. An apparatus for automatically and rapidly analyzing cells on a support, said apparatus comprising:
a plurality of processing means; means for producing an optical image on an imaging means of a field of the cells on the support; means for converting the image to a point by point distribution representative of the image;
means for converting each point into a digital signal;
means for controlling said imaging means by a first of said processing means so that a plurality of fields may be imaged on said imaging means; means for analyzing the digital signals of each field of the cells imaged by a second one of said processing means and for measuring characteristics of the digitized signals for predetermined red blood cell characteristics; and means for reporting parameters relating to the measured characteristics of the cells analyzed.

41. An apparatus for automatically and rapidly analyzing cells on a slide comprising: a plurality of processing means; means for producing a first optical im-age on an imaging means of a field of the cells on the slide; means for converting the image into a point by point distribution representative of the image; a digitiz-ing means for converting each point into a digital signal;
means for controlling said imaging means by a first one of said processing means so that an additional field may be imaged; means for analyzing the digital signals of each field of the cells imaged by a second one of said proces-sing means for a plurality of cell features; means for synchronizing the first processing means with said digitiz-ing means so that said first processing means may cause an additional field to be imaged after the first image is digitized.

42. An apparatus for automatically testing blood for abnormalities by analyzing red blood cells hav-ing quantifiable features in a blood specimen, said ap-paratus comprising: means for identifying at least one subpopulation of the red blood cells in said specimen;
means for determining a distribution of the red blood cells for said subpopulation with respect to a plurality of the quantifiable features of the red blood cells; and means for reporting parameters relating to said distribu-tion to provide an indication of a blood abnormality or a lack thereof.

43. An apparatus in accordance with Claim 42 in which said means for determining a distribution deter-mines cell size and cell hemoglobin and the distribution is a bivariate distribution with respect to the two quantifiable features of cell size and cell hemoglobin.

44. An apparatus for classifying red blood cells in a blood specimen comprising: means for examin-ing a plurality of red blood cells for identifying fea-tures including size, outer perimeter shape, hemoglobin content, and central pallor size; means for classifying the red blood cells by their outer perimeter shape, cen-tral pallor and a lack thereof features into subpopula-tions; and means for determining and reporting size, hemo-globin, and central pallor size parameters for at least one of said red blood cell subpopulations.

45. An apparatus for automatically classifying blood and its relationship to recognize categories of anemia wherein a sample of red blood cells having quanti-fiable features is analyzed, said apparatus comprising:
means for identifying at least one defined subpopulation of red blood cells; means for determining the proportion of said subpopulation in said sample; and means for com-paring said proportion to the proportion of a similarly defined subpopulation in a sample from a recognized cate-gory of anemic red blood cells.

46. The apparatus of Claim 45 in which said means for determining determines a distribution of the red blood cells of said subpopulation with respect to a plurality of the quantifiable features of the red blood cells and said means for comparing compares parameters re-lating to the distribution of the subpopulation to the par-ameters of the distribution of the similarly defined sub-population in the sample of anemic red blood cells with respect to the same plurality of quantifiable features.

47. An apparatus for automatically classifying blood and its relationship to recognize categories of anemias wherein a sample of red blood cells having quanti-fiable features is analyzed, said apparatus comprising: a means for identifying at least one defined subpopulation of red blood cells; means for determining a distribution of the red blood cells of the subpopulation with respect to a plurality of the quantifiable features of the red blood cells; and means for comparing parameters of the dis-tribution to the parameters of a distribution of a simi-larly defined subpopulation in a sample from a recognized category of anemic red blood cells.

48. An apparatus for automatically classifying blood and its relationship to recognized categories of anemias wherein a sample of red blood cells having quanti-fiable features is analyzed, said apparatus comprising:
means for determining the average of a plurality of said quantifiable features including the pallor size from said sample; and means for comparing said averages to the aver-ages of the same quantifiable features of a sample from a recognized category of anemic red blood cells.

49. An apparatus for automatically identifying target cells in a sample of red blood cells comprising:
means for determining a first cross-sectional profile of a red blood call and for determining a second cross-sec-tional profile in a direction substantially transverse to said first profile, each of said profiles relating to the thickness of the red blood cell at points along each cross section and defining relative maxima and minima; and means for detecting the existence of three relative maxima and two relative minima on either profile.

50. An apparatus for automatically analyzing red blood cells in a sample of a patient's blood comprising:
means for examining the red blood cells in patient's blood sample; means for measuring a plurality of character-istics of each of the red blood cells; means for determin-ing parameters of the multivariate dispersion of the dis-tribution of the measured plurality of characteristics for said red blood cells; and means for reporting on said par-ameters to provide a description of the blood.