CA1161271A - Method and apparatus for measuring mean cell volume of red blood cells - Google Patents

Abstract

METHOD AND APPARATUS FOR MEASURING
MEAN CELL VOLUME OF RED BLOOD CELLS
ABSTRACT
An apparatus and method are provided for producing signals representative of the mean cell volume of red blood cells in a blood specimen. The apparatus includes means (310) for generating signals representative of the area of the cells and means (318) for measuring the optical density of the individual cells and for generating signals representative of the hemoglobin content or mass of the cells. The central pallor is measured for cells having central pallors and means (264) generate a signal representative of the central pallor of these cells. The representative area signals, hemoglobin signals and central pallor signals are sent to a means (403, 415) which generates an output (430) representative of the mean cell volume of the cells.

Description

1 ~ 6~ 27 1 METHOD AND APPARATUS FOR MEASURING
MEAN CEL~ VOLUME ~F RED BLOOD CELLS

This invention relates to a method oE and an apparatus for measuring and reporting si~e information about red blood cell.s and particularly the mean cell volume of red blood cells in a blood specimen.
In applicant's Canadian Patent No. l,135,979, issued November 23, 1982, there is described various apparatus for automatically analyzing blood and providing representative output data of the mean cell size, mean cell hemoglobin, and mean cell density~ The mean cell size information generated and reported was expressed as area tu ) rather than Yolume (u3) the latter being the conventional size information generated wi-th conventional measuring technique such as the Coulter kind of particle counting apparatus. Since physicians are most familiar with mean cel] volume data than with mean cell area data, there is a desire to generate a mean cell volume output that can aid physicians and may also aid in automatic diagnosis of anemia or other blood disorders.
Some work has been done with image analysis and pattern recognition techniques to compare mean cell areas with mean cell volumes. Such equipment has not been very accurate in correlating with mean cell volume data generated by conventional Coulter particle sizing equipment used for blood analysis. The fault for this descrepancy may not be entirely with the image analysis equipment. As is known and has been reported in the literature, the Coulter blood counting equipment suffers from several shortcomings such as producing signals which are in error of a true blood cell volume for cells which are (1) tumbling as they pass through the measuring aperture, (2~ passing very close to the aperture wall, (3) in reality a pair of closely adjacent cells in the aperture rather than a single cell, (4) being measured while a previously measured cell is closely mg/~e "i~;y.
~.

~ 36~2~
adjacent the exit end of the aperture and is generating a ghost signal, etc.
The Coulter blood cell analyzing equipment is calibrated using spheres o~ a known size~ However, the blood cells are flattened and thin and many red blood cells contain thin central areas called central pallors wnich reduce substantially the volume of the cell from the ~olume the cell would have if it did not have a central pallor~ The change in size of central pallors of red blood cells appears to be a good indicator of changes in the blood disorders, as discussed in Canadian Patent No. 1,135,979. Hydrodynamic forces used in focusing the blood cells and passing the cells through the aperture of the Coulter cell sizing apparatus change the shape of the thin flexible cells from the shape the cells have in their natural relaxed stateO To compensate for ~arious ones of these factors, the Coulter counter is provided with a shape factor, so that the actual measured signal from the measuring aperture is multiplied by this ~aetor to obtain the final value of mean cell volume. It is thought that this shape factor is about 1.4 for today's eonventional Coulter equipment~
Another reason ~or preferring to generate mean eell,volume data for red blood cells rather than mean cell area is a better segregation of the data into more meaningful and more discrete patterns for blood order diagnosis. Moxe specifically, eells may be substantially similar in area and differ markedly in volume beeause of eentral pallor size or a lack o~ central pallorO
For example, normal blood cells and iron defieiency cells each typieally have sizes elustered in about the ~0 to ~0 square micron range and it is not possible to distinguish such cells from one another mg~ - 2 -~i''' ~ ~ 6:~ 2~ :~

on the basis of area. However, these same normal cells should have volumes clustered in range of about 75 to 100 cubic microns which is substantially different than the range of about 50 to 75 cublc microns for the iron deficiency cells. A graphic illustration of the plot of mean cell area by an image analysis technique versus mean cell volume from a Coulter counter type of sizing apparatus is set forth in FIGURE l of an article of "Bentley, S A. and S.M. Lewis, 'The Use of an Image Analy~ing Computer for the Quantification of Red Blood Morphological Characteristics', Brit. J~ Haemat~ 29:81, 1975". The cells used in this study were selected manually from each patient specimen and were processed with o$f-line general purpose computer equipmentO
~nlike the apparatus used in that work, a commercially practical image analysis system must be automatic, and competitive in speed and cost with the Coulter system in obtaining information on cells.
The Bentley and Lewis technique, the Wintrobe Indices technique, and the Coulter counter technique all provide size information for the total blood cell count and cannot make and correlate size data for a given kind of cell or for a given subpopulation o~ abnormal cells. With the equipment described in Canadian P~tent No. 1,135,979, it is possible to segregate and to measure the size of individual cells as well as the mean size for various abnormal cell subpopulations such as spherocytes, macrocytes, microcytes, etcO However, as above explained, the area differentiations are not as separated as volume size characteristicsO By providing mean cell volume for subpopulations of abnormal cells on a large scale basis, new insights should be gained into the cell volume characteristics of a given blood disorder and ~hould lead to more suhstantial and more accurate information on the volume relationship of mg/~ ~ 3 -` ~ ~

~ ~ B ~

abnormal cells to normal cellsO For example, very specific data as to volume differences for microcytic cells versus normal cells because of the difference in the thicknesses (the microcytic cells being thinner) as well as the differences in areas at various times from a patient undergoing treatment may provide an insight as to the effectiveness of the treatment at a very early stage.
The present invention may also be used to correlate the sizes of cells measured into different classes or categories such as microcytic, normocytic, o~ macrocytic with the cell si~e measurements obtained wi-th conventional techniques. ~eretofore, cell sizes have been measured and classified into these classes by image analysis techniques but the results have been poor and did not correlate sufficiently with the results from conventional equipmentO
Accordingly, a feature of the invention is to provide a new and improved method and apparatus Eor measuring the size of red blood cells.
Another feature of the invention is to provide a method of and an apparatus in using a central pallor analysis on the red blood cells having central pallor as part of the determination of the mean cell volume for a specimen of cells.
Another feature of the invention is to determine the mean cell volume of a total population or of a particular subpopulation of red blood cells.
The invention relates to a method of determining the mean cell volume of red blood cells of a particular subpopula-tion from a blood specimen, comprising the steps of:
examining a plurality of red blood cells in the blood, in 'r~ . m9/t~

2 7 ~

blood specimen; classifying individual red blood cells by multiple respective features thereof into a plurality subpopulations; and determining the mean cell volume for a given subpopulation of red blood cells.
In its apparatus form, the invention relates to an apparatus for determining the mean cell volume of red blood cells of a particular subpopulation from a blood specimen, the apparatus comprising: means for examining a plurality of red blood cells in blood specimen; means for classifying individual blood cells by their individual shape and pallor features into a plurality of subpopulations; and ~eans for determining the mean cell volume for a given subpopulation of red blood cellsa The features and advantages of the present invention will become apparent from the following detailed description taken in connection with mg///,~ - 4a -~ 3~27~

the accompanied drawings in which:
FIGURE 1 i.s a block diagram of the preferred embodiment of the invention;
FIGURE 2 is a graph illustrating the mean cell volume data as generated by the present invention and by conventional equipment;
FIGURE 2A is a graph illustrating a comparison of mean cell area and mean cell volume for the same blood specimens indicated in FIGURE ~.
FIGURE 3 is a perspective view of an apparatus ~or practicing the method and embodying novel features of ~he invention:
FIGURE 4 is a block diagram showing the operation of the apparatus illust:rated in FIGURE 3;
FIGURE 5 is a block diagram o~ the preferred process for analy~ing and classifying blood cells and for determining mean cell o~ volumes;
FIGVRE 6 illustrates a ~;canning techni~ue fur locating cells and determining the boundary points of ~0 cells in an im~ge;
FIGURES 7a, 7b, and 7c are flow charts of the preferred techniques for classifying the blood cells into mutually exclusive subpopulations;
FIGURE 8 is a diagrammatic view of a model for red blood cell central pallor measurement~
FIGURE 9 illustrates a chain code description and analysis method for three diagrammatic red blood cell outlines;
FIGURE lO is a block diagram of the preferred 3n process ~or determining whether a cell is round;
FIGURES lla, llb, and llc are graphs i.llustrating thickness/density profile measur~ments for three dif~erent, typically appeariny cell types, measured in two ortho~onal directions. These pro~iles are used to measure ~he cel:L central pallor ~eatures and 2 7 :3 target cell features, Figure llc illustrating a "flat"
cell having little or no central pallor development;
FIG~RES 12a, 12b and 12c are graphs illustrating the profiles of the cells of Figures lla, llb, ana llc with the peaks and valleys oE each profile labelled.
FIGURE 13 is a schematic of the preferred process for accumulating red blood cell subpopulation parameters;
FIGURBS 14a, 14b, 14c, 14d and 14e are schematics illustrating the preferred process of computing the subpopulation characteristics from the accumulated values from a plurality of cells; and FIGURE 15 is a logic section for generating a mean cell volume report.
As shown in the drawings for purposes of illustration, the invention is embodied in an apparatus 10, such as disclosed in U.S~ Patent No. ~,199,748.
In this equipment, as described fully in U.S. Patent No.
4,199,748, and as shown in FIGURES 3-6 herein the apparatus 10 comprises a microscopic digital image processing and pattern recognition system which analyzes a monolayer of red blood cells on a microscope slide 12 with the cells being spaced from each other to ease the automated classification thereof. Suitable high resolution microscope optics 1~ form an optical 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 poin-ts in each image. The output of the vidicon camera is applied to digitizer electronics 20 which includes an analo~ to digital-converter which is connected to an image mg/~ - 6 ' 11 ~ 61 27:~

processing logic ~2 which controls the digitizer electronics 20 and receives and stores the digitized cell images into a memory store. The ima~e processing logic 22 operates on the digitized cell images in a manner that will be hereinafter described which includes cell feature extraction and cell classification.
A suitable stage motor means 24 is provided and controlled by s~age motor electronic 26 which are in turn controlled by a master control logic 28. The stage ~0 motor 24 is provided to ~hift the slide 12 in order to iteratively process different image areas of the blood specimen on the slide. To control the focus of the microscope, a focus control motor means 30 is connected to the microscope and is operated by foCU5 motor electronics 32 which are also controlled by the master control logic 2~ by means of the focus p~rameter electronics 34. Focus control oE 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 3 includes a housing 38 having a cover 40 enclosing the microscope optics 14 and the television vidicon 16. An upper section 42 of the housing 38 houses the control switches o 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 ~or the image processing logic 22 and master control logic 20 and the motor electronics 26 and 320 A terminal 48 is 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 aE~ord a permanent record. A TV
monitor 55 provides desired pictorial displays, The TV

~ 1 B~2~:~
camera electronics are housed in a section A9 below the monitor. The next lower 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 transmitted for storage in a medical computer data bankO
Red blood cells may be examined such that normal cells are distinguished from abnormal cells and classified by the apparatus 10 into subpopulations automatica~ly in a detailed fashion heretofore not possible by a manual/visual examination of cells. Also, each of the red blood cells being examined may be classified into mutually exclusive subpopulations and reported out so that the presence of a minor number of abnormal cells is not overlooked or forgotten and so that accurate parameters about a given subpopulation may also be provided. The individual red blood cells may be examined individually for the hemoglobin contents.
Thus, a report may be made not only of the kind o:E cells found in the subpopulation buk also of their number and their hemoglobin characteristicsO Advantageously, the individual red blood cells may be analyzed and classified with less subjectivity into a large number of mutually exclusive subpopulations such as biconcave (round cells with central pallor), elongated cells, targets, and irregular cells (cells not fitting into any of the above classifications).
The pre~erred he.moglobin characteristic gathared from the analysis of the hemoglobin c.ontents of the individual cells within a given subpopulation and reported out is the mean cell hemoglobin (MCH) for a given subpopulation of cells, such as shown in Table I
of Canadian Patent No. 1,135,979. In addition to the hemoglobin parameters, the individual cells are counted for each subpopulation to provide their respective mg/~ - 8 -r 11 3 B :~ 27 ;~

percentages of the total population; and likewise mean cell volume (MCV) for each subpopulation may also be reported out in a format such as shown in Table I in Canadian Patent No. 1,135,979. It has been found to be helpful in detecting abnormalities in blood samples -to determine multivariate distributions of the red blood cells in particular subpopulations of a sample with respect to a plurality of quantifiable features.
According to the method described in U.S. Patent No. 4,199,748, red cell size was measured as the projected area of the red cell in square microns. This is a two-dimensional description of size and does not contain any volumetric information regarding size, such as the thickness of the cell, or a decrease in the volume of the cell due to increased central pallor. It was not evident prior to the invention that a projected area measurement of "size" in square microns, was not equivalent in a diagnostic sense to a volume measurement of "size"
in cubic microns, such as that which could be obtained with a Coulter counter or the like, or by determining the hematocrit value of the blood sample and then dividing by the red cell count.
Experimentation with normal and several types oE
anemic blood specimens have recently indicated that the diagnostic information relati~e to size is better preserved as a volume measurement rather than an area measurement. This can be better understood from FIGURE 2A, in which are compared cell size measurements from the blood of persons with iron deficiency anemia, megaloblastic anemla and normal blood. Size measured by an electrical impedance apparatus, in this case the Coulter Model S
(MCV) is compared to the area analysis (MCA) from the image analysis equipment disclosed in the aforesaid co-pendin~ application. Notice that if the results are projected on the MCV axis there are three mg/)~ 9 -6~

-ln-distinct clusters i~e. a separation of data; whereas, if the results are pro~ected on the MCA axis these distinctions are not as apparent. This indicates that an inclusion of volumetric information is desirable when S reporting a measure of red cell size.
In accordance with the present invention, mean cell volumes are generated which take into acccunt the central pallors of the red blood cells to provide data which may be directly correlated with MCV data generated in the past or presently being generated by sonventional equipment using conventional Coulter counter equipment.
This is achieved by using central pallor data or central pallor signals in combination with the area and hemoglobin characteristic data or signals to generate an output representative of the mean cell volume which takes into consideration the actual volumes of the individual central pallors (i~ any) of the cells being measured. As diagramatically il:Lustrated in FIGURE lS, representative signals o~ mean cell area (MCA), mean cell hemoglobin (MCH), and mean cell pallor (PAL) are generated ~lith the above described apparatus and are sent to a means which generates an output representative of the mean cell volumes for the blood cells. The accuracy o~ the present invention in providins mean cell volume as related to similar measuremen~s from a Coulter counter instxument is readily apparent ~rom a consideration of FIGURE 2. The data from the image analysis is substantially similar on the ordinate for the volume on the abscissa for the Coulter counter 3~ measurements of the same blood samples.
To achieve this MCV measurement four parameters ~1, K2, K and ~4 are used in connection wi~h the measured values o~ MCA, MCH ancl PAL, with the parameters Xl, K2 and K3 each being a multiplier for these measured values as indic~ted in FIG~E 15. The values ~or Xl, K2 ; .

11 ~ 6:~27 :L
and K3 have been determined experimentally as will be explained hereinafter. A fourth factor K4 is added to the sum o~ MCA(K13 ~ MCH(K2) -~ PAL(-~3) and is an offset factor indicating the amount of offset from the juncture of the abscissa and ordinate of a plot of the MCV'sO
This offset is thought to ~epresent a factor due to drying of the blood cells prior to image analysis with the apparatus 10 disclosed herein. As will be explained, in the described embodiment of the invention, these values are Kl--.43; K2=1.94; K3=-.~4 and K4=27~ The preferred means for determining mean cell volume comprises either a digital logic system of electrical devices or a programmed microprocessor which uses Boolean logic.
In the analysis given in Canadian Patent No.
1,13S,979, the cells are classified into subpopulations related to a specific anemia such as set forth in Table I therein. In U.S. Patent 4,097,845, the subpopulations given in Table I were into hematologically recognized subpopulations such as normocytes with central pallvr, normocytes without central pallor, spherocytes, etc. with the size of the cells being listed as mean cell area in square micronsO In this same patent several examples were printed out. Mani~estly, the mean cell si~e may be printed out for the entire population as well as for a given subpopulation.
Likewise, with only slight modification of the analysis logic described herein the mean cell volumes given hereinafter ma~ also be given ~or subpopulations as well as for the to~al population.
~ o aid in understanding classification of the cells as well as the measurements used to classify the cells prior to determining the mean cell volumes therefor, some of the description given in the aforesaid U.S. Patent No~ 4,199,748 will be repeated. As will be mg/~ - 11 -, .

~ 1 ~127:~

explained, the present invention is capable of reporting the total population and the average mean cell hemoglobin as well as the average mean cell volume for the entire population, the average mean cell hemoglobin may be reported out in the line with average parameters of Table I of Canadian Patent No. 1,135,979. Thus, as indicated above, herein the invention will be described as having the ability to classify red blood cells into the several mutually e~clusive subpopulations set forth in Table I of the aforesaid applicationO The subpopulations listed therein are preferred subpopulations for classifying blood with respect to recognized categories of anemias but there may be other subpopulations defined. It is also possible to provide a mean cell hemoglobin for a subpopulation of cells, such as biconcave cells, as will be explained hereinafter. Additionallyl it is possible with modifications of the analysis logic to determine the cell volume for each cell and then to subsequently determine the mean cell volume for the total population or ~or any given subpopulation of course, the size classification of the cells may be reported out in other manners such as microcytic, normocyticl ox macrocytic.
The present invention is also of utility in correlating other and existing equipment which has not performed adequat&ly in reporting out cell size classifications as microcytic, normocyticl or macrocytic for the reason that the reported classification did not match the results obtained with conventional equipment.
As disclosed in the aforesaid V~S~ Patent No.

4,199/748, a multiple parallel logic architecture has been found to provide the rapid processing necessary for e~ficient analyzing of cells on a slide. Herein, there is provided a first processing means, the master control logic 28 ~FIGURE ~), and a second processing means~ the mg~Q 12 , ~

27:~

image processing logic 22 as shown in FIGURE 4. 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 previous operation,

5 there are provided synchronizing means for synchronizirlg the processors so that the results necessary to perform a particular operation are available when that operation is begun.
FIGURE 5 illustrates the specific interrelationships between the master control logic ~8 and the image processing logic 22. Because of this multiple parallel logic or architecture, the master control logic may proceed with one taslc or operation while the image processing logic is proceeding with another operation.
As seen in FIGURE 5, the operations carried out by the master control logic 2R are listed in the lefthand column with the operations of the ima~e processing logic 22 in the righthand column. The master con~rol logic, af~er 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 6D) from the master control logic.
Vpon receipt of the start signal, the image processing logic 22 proceeds to operation 62 which includes digitizing the image produced by the vidicon camera 16 (FIGURE 4). Upon completion o~ the digitizing, the ima~e processing logic sends a "digitizing done" signal ~operation 64) to the master control logic indicating the completion of the digiti~ing process and proceeds to operation ~6. The master control logic operation 58 is ~urrently w~iting for the "digitizing done" signal and upon its receipt proceeds to move the stage (operation . ................................................. .

27:~

60) on which the slide rests so that a new field of cells may be imaged since the previous field has already been digiti~ed by the image processing logic 22. The optics 14, FIGURE 4, are providing an imaging means of the cells on the slide. The stage motor drive 24, and the focus motor drive 30, and their associated electronics, are controlled by the master control logic 28. After moving the stage so that a new field may b~
imaged, the master control logic proceeds to 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 a cell boundary point ~operation 66). If a cell boundary point is found (operation 743, the image processiny logic extracts the cell's boundary and features (operation 76) and classifies the cell as to its proper subpopulation (operation 78).
The image processing lo~ic then returns to operation 66 and continues scanning the image for another cell boundary point. The scanning, feature extraction, and cell classification operations will be described in more detail below. If the logic secton 74 determines that a new boundary point has not been located, then the image processing logic proceeds to operation 80 wherein the features o each cell located as well as each cell's subpopulation classification is transmitted to the master control logic which will be in the process of executing operations 68, 70, or 72. The transmittal of the information is on an interrupt basis, i.e., should the master control logic be in the process o controlling the imaging means (operations 68 or 70), the master control logic will interrupt these operations and store the information received from the image processing logic before proceeding with moving the stage 1 ~ 6~ 27 :~
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 from the imaqe processing logic. In response to the receipt of the data, the master control logic will transmit 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, as will be more fully explained below~
Upon receipt of the acknowledge signal, the image processing logic proceeds to digitize the image of 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 equal to 1000. If 1000 cells have not been processed, the master control logic returns to operation 58 and waits Eor the "digitizing done" signal from the image r 20 processing logic, otherwise the master control logic calculates the subpopulation parameters (operation 90) proceeds with a means cell volume (MCU) determination (operation 100) and prints the results (operation 102~, as will also be more fully explained below. The apparatus may be used to provide an output of an anemia classificati~n as described in Canadian Patent No. 1,135,9~9, or the present invention could be made a "stand alone" unit whose only function would be to provide mean c.ell size (~CV) for a total specimen without having to do any classifying into subpopulations, or anemia classifications.
Thus, because of the dual processor architecture, the master control logic is free to m~ 15 -.

2~1 1 control the imaging means wherein a new field is brought into view to be imaged 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 accumlating 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 notd that although for purposes of illustration only one image processing logic is described as associated with the master control logic J
it is capable of utilizing information from several image processing logics operating in parallel and ]5 independ~ntly on different images.
With the present invention, the optimization of the time o analysis as well as the number of ~eatures used in the classi~ication logic is achieved so that the amount of storage and classifyillg techniques may be reduced substantially along with equipment requirements therefor. With an optimization of analysis time for classification, there is a danger that the reliability and accuracy of the calssification are comprised.
Despite this, a relatively foolproof feature set and classification logic has been invented for a large number o~ subpopulations such as those shown in Table I
in the aforesaid application. The preferred classification features are size, hemoglobin content, spicularity, roundness, elongation, central peak height ~if present) from cross-sectional ce]l scans~ and central pallor. By suitable combinations and analyses of such features, it is possible to dif~erentiate ~rom normal blood and to identify biconcave round cells, spherocytesl target cells, irregular-shaped cells, and elongated cell~.

..

1 11 6~ 2~

In the preferred method and apparatus, the cell classifica-tions are achieved by an image processing and pattern recognition with great accuracy and reliability by rendering white blood cells and other artifacts substantially invisible to the optics 14 by using a light having an optical wave length of about ~15 Nanometers. At this optical wave length, the red blood cells and other formed elements are substantially invisible. The staining of the red blood cells prior to being analyzed 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 stained artifacts which detract from the accuracy of the analysis. Furthermore, many of the stains are not stoichiometric in the representation of hemoglobin concentration according to density, thus distorting the quanti~ation of the hemoglobin content of the cell on a per-cell basis. A particular manner of vapor fixing of cells before they dry without staining thereof to prevent the formation oE artifacts by distortion of the central pallor i9 disclosed in U.S.
Patent No. ~,209,548 entitled "Method or the Preparation o~ Blood Samples for ~utomated ~nalysis", issued June 2~, 1980. Thus, by rapidly preparing the specimens to a monolayer and fixing with a formaldehyde vapor prior to the drying of red blood cells, as disclosed in Canadian Patent No. 1,135,979, 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 identification and feature extraction has been greatly simplified as described below to locate and define the mg/~ - 17 -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 extraction as to: (1) cell size, (2) perimeter length and roundness shape measurel (3) irregular shape measure, and ~4) elongation shape measure. This is followed by extracting 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 boundaries are determined and features analyzed for more precise target cell identi~ication.
After having extracted these identifying features, the cells are then categorized by a classification means. The preferred classification means (FIGURES 7a, 7b, and 7c) compxise either a digital logic system of electrical devices or a progr~mmed microprocessor which uses Boolean logic to classify the red hlood cells.
Referring now in greater detail to the speci~ic features of the illustrated embodiment of the invention, the images o~ the cells are digitized (operaton 62 of FIGURE 5) in a manner known to the art, eOg., U.S.
Patent No. 3,883,852 as a television digitizin~ system.
Magnified blood cell images are obtained by using microscope optics with ultraviolet illumination, arranged to provide a 0.23 pixel resolution in the imag~
plane. A pixel is a picture element having a speci~ic location in the digitized image stored in the memory analyæer.
Re~erring now to FIGURE ~ which illustrates in 2 ~ ~

greater detail the operation 66 (FIGURE 5) by the image processing logic, an oriqinal 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 represented by the blocks indicated at 115 which comprise the operations 76 and 78 (FIGURE 5). In this preferred embodi~ent 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 kechniques which are well known in the art.
One such technique is disclosed in U.S. Patent No.
3,315,229. During this counterclockwise boundary tracin~ 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.
The raster scan o~ the digitized image is then continued from the bottom pixel 114f to hit the next digitized cell 112 by impacting a pixel 112a which is above the threshold as seen in block 116. After the boundary is traced and the features for this cell are extracted and the cell is classified, the raster scan continues from the bottom pixel 112b, and~ as seen in block 118, no more cells are located in the image field. At this timel the image processin~ logic tr~nsmits the cell features and subpopuLation ~ 1 ~1271 classifications to the master control logic (opera-tion 80) as shown in FIGURE 5.
The initial image processing done by the image processing logic outlined in FIGURE 5 is shown in greater detail in FIGURE 7a. 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.
During this boundary tracing operation, octal chain codes are formed in an operation 119. The outer boundaries, defining a cell, are processed in the following 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 9, a t~igital image of cells as defined by their boundary pixels 120 are illustrated.
As is well known in the art, e.gO, as described in "Bacus, J.W. and J. H. Weens, 'An Automated Method of Differential Red Blood Cell Classification with Application to the Diagnosis of Anemia', Journal of Histochemistryr and Cytochemistry, 25:7, 1977", a plurality of features Fl-F~ can be computed from this chain code. The details of this computa-tion are fully described in the aforementioned publica`tion.
The above features are combined with other features for use in the claissification of the cells. In this regard, the following features are used herein:

mg~ - 20 -~ 363L2~

Table II
Feature Descri~tion How Determined Fl Area size Number of pixels enclosed by cell boundary F2 Shape Icircularity) (Number of perimeter pixels)2/area F3 Shape (spicularity) Number of "spiculesl' on boundary F4 Shape ~elongation) Comparison of orthogonal houndary chain code orientations F5 Grey levels Sum o~ grey levels as a measure of Cell Hemoglobin 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 pro~ile, the dif~erence o~ the valley ~rom the peak heights 7 ~L

F9 Pallor ~circularity~ (Number of pallor boundary pixels) /
area of pallor As indicated above, features Fl-F4 are calculated in an operation 124 by the image processing logic as shown in FIGURE 7a. Feature Fl relates to the area or si~e of the cell as determined by the number of picture elements or pixels that are enclosed by the cell boundary. Feature F2 is the ~boundary perimeter)2/area and is of assistance in classi ying 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 lnvolves quantization error, such that in practice ~or quanti~ed circles the value approximated is 14.0, and is a be~ter approxima~ion to this reference number as the number of pixels, or size, of an object increases.
Features F3 and F4 relate to the spicularity and elongated shapes, respectively, F3 being a count of the number of spicules in a chain code boundary, and F4 ~5 measuring the non-rolndness due to elongation of the boundary, as sho~n in FIGUR~ 9. Feature FS is the integrated optical density of the cell (operation 136j.
It is the sum of the grey levels within the enclosed boundaries of the cell. Feature F6, which is a measure of the pallor volume, assists in distinguishing cells ith large pallors, such as hypochromic cells rom normocytes. Feature F7 is equal to the larger of the two central peaks of two rross-sectiollal orthogonal 3-peaked thickness/density pro~iles, eikher having central peak, and is used to detect target cells.

. .

~ 3 ~ ~ 2~ ~

-~3-Feature F~ is a measure of the depth of the central pallor, as determined from two cross-sectional, ortho~onal, ~-peaked thickness/den.sity profiles.
Feature F9 is a measure of the deyree of roundness of the pallor itself t and is also usea in distinguishing target cells.
The logic decisions for determining the various features that have been briefly described ~re carried out by the image processing logic using the logic flow chart shown in ~IGURES 7a, 7b, and 7c. The logic decision are made using the various features together with threshold values that are identified as Tl through Tll. The thresholds 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 flaw chart shown in FIGURES 7a, 7b, and 7c. In this regard, FIGURES 7a, 7b, and 7c illustrate various decisions that are made on the basis of various features either exceeding or being less than certain threshold values as will be .specifically described.
Referring to FIGURE 7a, an object that is located is examined by logic section 138 to determine if it is sufficiently large to be a cell, rather than a noise or dirt artifact, and thus is to be further analyzed. If feature Fl, which is the size or area of the object under consideration, 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 object will be located for analysis and classification. However, if the area of the cell is greater than the threshold value Tl, feature F5 is computed in operation 136 wherein the hemoglobin content o~ the cell is determined. This is simply a ~ ~ 6~27~

summing of the grey levels inside the boundary of the ehain coded cell and then dividing by a eonversion factor 12g0 or thereabout to convert the grey level measurements to picograms o~ hembglobin per cell.
For this purpose the electronics generating the television signal and digiti~ing said signal should be adjusted to produce grey levels corresponding to the following optical density at 418 nanometers:
Table III
10Optical Den~ rey Level ~13~ 17 .294 35 ~403 52 .505 43 15.605 57 Also, ~or calculation of hemoglobin and the area, the optics and television electronics should be adjusted such that round objects o~ the following dimensions produce the given number of pixels.
Table IV
Size 2 Pixels ~5 58 g67 34 5~7 The decision logic then operates to determine whether the cell is round or non-round. This is performed by a logic section indicated generally 140.
the logic section 140 is shown in FIGU~E 10 to include logie subsections 1~2, 144, and 146. The subsections ~ ~ ~12~ ~

142, 144, and 1~6 are operable to jointly make the roundness determination wlth the features F2, F3, and F4 being examined 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 7a) which is the first step in the target cell analysis and central pallor analysis.
~imilarly, if it is determined that the cell is not round, then logic subsection 150 (FIGURE 7a~ operates to determine if the size of the cell exceeds an upper boundary threshold T2, and if it does, the cell i5 not further analyzed and a new cell will be considered. The effect of the subsection 150 is to eliminate double cells such as that shown in the pictorial representation 152. It should be appreciaLed from the pictorial representation that such a double cell would not pass the roundness test, hut it is also not a nonround cell of the type ~or cells of classes 3 and 4. Thus, it cannot be accurately classified and it is for this reason that the subsection 150 eliminates such cells from further consideration.
As previously mentioned, the roundness of the cell is determined by feature F2 which will have a value of 14.0 for a per~ect circle and will increase as the shape of the cell departs from circular. Thus, the threshold value ~4 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 subsection 150 indicating that the object is not round. If feature F2 is not greateL than threshold T2, it is one indication that the cell is round and if the decision from the subsections 144 and 146 al~o indicate adequate ~S roundness, the logic Elow then proceeds to logic ... .

~ 1 6;~ 2~ :~

subsection 148 (FIGURE 7a).
In operation 148 thickness/density profiles are extracted from the cell image. These profiles are illustrated in FIGU~S lla-llc and 12a-12c. ~ thickness density profile is determined by the grey levels of the pixels along a particular direction across the cell image. As noted earlier, the grey level of a pixel is determined 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 ~nd the cell thickness at that point. Two such thickness/density profiles, proile a and profile b, are shown in E~IGURE lla for a biconcave cell determlned in two orthogonal or transverse directions, a and b. Two profiles each are also illustrated in FIGURES llb and llc for a target cell and a spherocyte cell. As seen in FIGURE llb, one direction (direction a) practically missed the center area. Since these profiles are used to dis~inguish target cells (feature F7), two transverse directions are preferably an~lyzed. Thus Eor each cell, two cross-sectional pro~iles are determined ~herein the profile relates to the thickness o the cell along the points of the cross seckions.
A profile for each cell o FIGURE 11 is discussed more fully in connection with FIGURES
12a-12c. As seen in FIGURE 12a, the profile has two l'peaks", Pl and P2, and one "valley", Vl. Pl and P2 are relative maxima of the profile o~ the cell with respect to the cell thickness and thus determine the two relative maximum ~hickness density points along the profile. Vl determines the relative minimum point o~
thickness d~nsity. Similarly, the target cells have three relative maxima, Pl, P2, and P3, with two relative minima, Vl and V2, as shown in FIGURE 12b. The spherocyte has one peak, Pl, and no valleys ~FIGURE

.,i . . .

2 7 ~

12c). These 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 thickness/density profiles for the cell, it proceeds ~o the target cell analysis performed by the logic section, referred to generally at 156 of FIGURE 7b. The Eirst step of the target cell analysis is to smooth the two profiles, profile a and profile b, as shown in operations 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 peaks, Pl and P3, or "LEVl". A logic subsection 164 determines whether the two valleys, Vla and V2a, are less then LEVl and i so then the cell located might be a target cell and the image processing logic proceeds to examine profile b. If not, then the ~alleys are not deep enough in proile a to be a target cell, so the center peak, P2a, is set to zero in an operation 16k and profile a is smooth~d to two peaks or less in an operation 168.
After proEile a is examined, profile b is examined for three peaks in a logic subsection 170. If the logic s~bsection determines that profile b has three peaks, it is ~orwarded to an operat;on 172 and logic subsection 174 wherein the two valleys, V2a and V2b, are compared to LEV2 which is hal~ the average of the two non-center peaks Plb and P3b as for profile a. If the two valleys are less than LEV2, then it is forwarded to operation 176 wherein the feature F7 is determined as to which is the larger of the two center peaks, P2a and P2b, of the pro~iles a and b. Fea~ure F7 is compared to a ~hreshold T7 in a 109ic subsection 178, and if larger, 27~

2~-the cell is classified as a target 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 o~ 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 peaks or less in operations 180 and 183. llowever, if the logic subsection 17~ determined that the valleys of profile b were not less than LEV2, then the profile b i5 ~orwarded to a logic subsection 181 which checks whether the center peak of profile 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 ~or F7 as described.
If the center peak, P2a, had been set to ~ero, then neither profile has passed the tests at logic subsection 164 and 174 respectively. Thus the cell is probably not a target cell and profile b is also smoothed to two peaks or less at operation 182.
However, some target cells might not be detected in this analysis, thereore, other tests are pPrformed 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 V]a are set equal to each other in an operation 138. In either case, the image processing logic then exanines profile b to determine whether it has only one peak, at the logic subsection 190. lf profile 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 7c therein, a feature 35 F8 r which i5 the average value o~ the two valleys subtracted from the average value of the four peaks of the two profiles of the cell, is determined by subsection 194. Then the cell feature Fl is examined to determine whether the size 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 r it is possible that the cell is a target cell despite the previous target cell analysis and therefore another target cell analysis will be performed beginning in operation 198. Therein, a variable ~E~3 is set equal to one-half the value of feature F8 (operation 198).
Next, a search for the central pallor of the cell is in1tiated by searching a directlon along the line from the top pixel of the cell through the center of the cell looking ~or 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. Fg is calculated as the number of pallor boundary 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 circularity feature F9 is greater than the threshold T9, then the cell is a target cell, otherwise the cell is forwarded to the operation 209 wherein a fea~ure relating to the size o~
the central pallor of the cell is computed.
The central pallor ~eature is defined as the percentage volume of a cylinder, with the height and area of the cell under consideration, not occupied by hemoglobin. this is illustrated in FXGIJRE a, where T

~ 3 ~X 2~

represents the cell height or thickness, and 132 indicates the indented central pallor region. The cell area is known from previous analysis on that cell, i.e,, Fl. Also, feature F5 is the sum of the grey levels for pixels enclosed by the chain code defining the boundary of the cell. As noted above, the hemoglobin density is related to the thickness of the cell and in this manner the hemoglobin feature F5 defines a volume of the cell.
The cylinder height, or thickness (T), is derived by using the average value of the peaks of the two ~hickness/density profiles of the cell as:
T - Pla = P2a + Plb ~ P2b Thus, the volume of the central pallor may be calculated as: T times ~he area of the cell (Fl) minus the hemoglobin content. Finally/ the percentage pallor volume F6 is:
F6 = (T x Fl - F5) x 100 T x Fl After this feature has been computed, the image processing logic proceeds to a logic subsection 210 wherein the cell is distinguished between biconcave cel]s (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 (C5). The logic subsection 210 compares the percentage pallor volume feature F6 to a threshold value T10 and the pallor depth feature F~ to a threshold Tll and if either feature is less than its associated threshold then the cell is deemed a spherocyte cell (C2), otherwise it is a biconcave cell ~Cl).
Referring back to FIGURE 5, the fPature extraction operation 76 and the cell subpopulation classiication operation 78 have been completed Eor the ~ 3 ~127~

cell that had been located in the image scan The image processing logic will then continue scanning the image for another cell (operation 6Ç) 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.
While the determination of the various features and decisions contai~ed in the logic diagram of E~IGU~ES
7a, 7b, and 7c is carried out utilizing the threshold values contained in Table V, it should be understood ~hat 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 values are believed to be optimum values which have been fixed to maximize the accuracy of the cla~sification.
Table V

Threshold Value Description Tl 6 2 Size threshold for arti~act T2 54 2 Size threshold for double cells T3 25 Elongated thxeshold T4 16 Cell cirularity threshold T5 7 Spiculed threshold T6 25 Elongation threshold T7 5 grey levels Tar~et center peak h~ight threshold T8 47 2 Size threshold ~or tar~et cells T9 20 Pallor circularity threshold 27~

T10 11% Pallor volume threshold Tll 8 grey levels ~pth of pallor threshold 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 logi~ as illustrated in FIGURE 5. After acknowledging tne receipt of the data (operation 82), the master control logic proceeds to update ~ubpopulation measurements for each cell class located in the image ~ust analysed toperation 84), A diagram illustrating the updating operation in greater detail is shown in FIGURE 13. A plurality of accumulators are provided to produce a running total of a plurality of measurements for the cell subpopulations or classes.
Each accumulation is a function of one or more cell features, such as the cell eature value itself or the value squared, for example. The cell feature values Fl, 20 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 ~or the cell have been accurnulated, 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 ~eature) is provided at a line 212 to an accumulator 21~. The accumulator 214 produces a running total Sl, i.e., accumulates the measurement (F2 - 14.133 for all the cells located by the image processing in logic wherein F2 i5 the cell circularlty feature (Table IV). This measurement is used in a la~er calculation which ~ 3 ~2~

provides a parameter describing the skewness oE the distribution of all the red blood cells loca~ed with respect to the circularity feature of the 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 all the cells which is used to calculate the average elongation for the cells. ~he accumulator 220 provides a sum or xunning total (S3) of the elongation eature 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.
In the aforesaid U.S. Patent No. 4,199,748, not all eature measurements were accumulated Eor each subpopulation. For example, in that patent the feature F6 (pallor volume) was only accumulated ~or the biconcave cells (subpopulation Cl) and the spherocyte cells ~subpopu].ation C2). Therefore, in addition to the Peatures for a particular cell, the suhpopulation classification for the particular cell to which the ~eatures pertain was provided as shown as Ci at line 222. A plurality of logic utili~e the input Ci to discriminate among the cell subpopulations. Thus, the cell classification Ci is provided to the inputs of a logic AND gate 224 and an AND gate 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 othe input oE AND
gate 226. The output o~ these AND gates are provided to on OR gate 228 which may enable the accumulators 230 and 232. The accumulator 230 provides a summation o~ the feature F6 (central pa:Llor volume) as indicated by input lines 242, but only when enabled by the logic OR gate 2~8. Similarly/ the accumulator 232 accumulates the sum .. . .

~ 1 61 2~ :1 c~ the feature (F6)2 but only when enabled. Thus, the gates ~24, 226, and 223 permit the accumulators ~30 and ~32 to accumate the measurements derived from the feature F6 only when the feature had been extracted from 5 a Cl or C2 biconcave or spherocyte class cell. The output of the accumator 232 is provided at S5 which is used to compute the dispersion param,eter of the distribution of spherocy~e and biconcave cells with respect to the mean volurne of the central pallor of the 10 cells. The output of the accumulator 230 is provided at S4 ~hich is also used to calculate the dispersion parameter and also to calcu:late the mean or average central pallor volume for the spherocyte and biconcave cells.
Similarly, a logic AND gate 234 enables accumulators 236, 23~, and 2~0 when Ci at line 222 is equal to a 2, i.e.l the cell feat:ures appearing on the feature lines 244 and 246 were extracted from a class C2 (sEherocyte) cell. The accumulator 236 accumulates the 20 feature Fl (cell area) which is provided at Sll, which will be used to calculate the mean cell area par~meter for the cells in the C2 classification. The accumu]ator 238 provides at S12 the accumulated total of feature F5 (cell hemoylobin content) which is used to calculate the 25 mean cell hemoglobin content for 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 local:ed by the image processing logic~
In a similar manner the total cell area for the 30 elongated (C3), the irregular (C4), and target (C5) cells are provided ~t S13J S15~ and S17, respectively, The total of all cells' hemoglobin content for the elongated~ irre~ular, and target cells is provided at S14, S16, and S18, respectively. The total number o;E
35 cells in each of the above subpopulations is provided at ~ ~ 6 ~ 27:~

~ 35~
N3, N4, and N5.
I.ike~ise, the total o~ all of the cells~ areas 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 biconcave subpopulation, additional logic gates permit 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 rom a Cl, i.e., a biconcave cell. The accumulator 250 provides the accumulated sum of the measurement (Fl) at S8. The accumulator 252 similarly provides the accumulated total o~ the measurement (F5)2 at S9. Finally, the accumulat~r 254 provides the accumulated sum of the product of the ~eature Fl times the feature F5 (Fl x F5). The accumulated S9 and S10 are used to calculate parameters descriptive of the dispersion, or varia~ion of ~he bivaria~e distribution which will be further explained hereinafter.
Thus the features for each cell examined by the image processing logic provide the inputs to the logic described in FIGURE 13 for updating or accumulating measurements based upon the cell features with the particular measurements updated for each cell depending upon the subpopulation classification to which that particular cell belongs. The measurements updated by the logic of FIGURE 13 may be used as an intermediate step for the calculation of parameters which are descriptive of each subpopulation classification as well as parameters which are descriptive o multivariate distributions of cell subpopulations with respect to di~erent cell features.
Referrin~ back to FIGURE 5~ it is seen that at 1 3 ~ 1 27 :~

-3~-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 whe~ein 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 13 for those N
cells are used to calculate the parameters descriptive of the subpopulations (operation 90) which is illustrated in greater detail in FIGURES 14a through 14e.
A parameter for the mean central pallor volume (P~L) o the bicvncave 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 oc the central pallors of those subclassifications). A
parametex o~ the distribution of the biconcave and spherocyte cells with respect to the central pallor volume, herein, the central pallor volume standard deviatic~n tPSD) is provided by a logic subsecticn 266 having inputs S4 and S5 and a logic subsection 268 which takes the square root of the output provided by the logic subseetion 256 to finally produce the parameter PSD in a manner similar to that of the parameter ESD.
~ eferring to FIGURE 14a, a logic diagram is shown for the computation of the parameters EVl and EV2. The ~eneral formula or computing the variance of a distribution with respect to a variable is ~imilar 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 illpUtS ~ (the number of biconcave cells), S3 (the summation of (Fl)2 for eaeh biconcave cell), and S6 (the summation of Fl for eaeh biconcave cell). The variance of the clistributiorl with .. ~

2 ~ ~

respect to hemoglobin content is provided by a logic section 272 which has inputs Nl, S9 (the summation of (F5) ), 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 for each biconcave cell), A logic section 280 squares the output of the logic section 278 to produce an output B. A logic section 282 subtracts the output A ~ the logic section 276 from the output B of the logic section 280 to provide an output D. K and D are coef~icients of a quadratic equation wherein a lc)gic section 282 produces the first solution, EVl, to the quadratic ~quation, and the logic section 2.84 produces the second solution, EV2, to the equation.
A logic section 286 produces the mean cell hemoglobin parameter for the biconcave cells by dividing the total 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 tS6) of the biconcave cells by the total number (Nl) of the biconcave cells.
In a similar manner, as shown in FIGURE 14b, ~he mean cell area and mean cell hemoglobin parameters are computed for the remaining four classes or subpopulations, i.e., the spherocytes, elongated, irregular, and target cells by either logic sections 290-~2~7. The number o~ cells in each subpopulation, Nl~N5, are each trans~ormed into a percentage oE the ~ 3 612~'~

total number of cells by five logic subsections 300-304, in FIGURE 14b. For example, the percentage of biconcave cells (I~Cl) is provided by logic subsection 300 which divides the number of biconcave cells (Nl) by a total number of cells located by the image processing means (N) and multiplies by 100.
Finally, in the preferred embodiment, two other parameters are calculated which describe the entire population o~ cells analyzed as illustrated in FIGURES
l~d and 14e. First, a mean cell area parameter (MCA) is calculated as a weighted average by mul~iplying the percentage o~ a subpopulation (i.e., NCl-NC5 being first divided by 100) by the mean cell area for that subpopulation for each subpopulation and adding the lS products to produce the weighted averageO For example, the percentage of biconcave cells (NCl) is multiplied by the mean cell area (MCAl) for the biconcave subpopulation by means oE a logic section 306 and the percentage of the spherocyte cells (NC2) is multiplied by ~he mean cell area of the spherocyte cells (MCA2) by means of a logi~ section 308 and so on for the other subpopulations and adding these five products by means of a summation logic section 310 to produce the mean cell area (MCA) ~or the entire population. A weighted average of the hemoglobin content for the entire population (MCH) is produced in a similar manner by a plurality of "multiply" logic sections 312-316 and a summation logic section 318.
As explained hereinbefore, the general method of measuring the mean cell size to provide the close correlation to the MCVs achieved b~ the Coulter counter, is shown in FIGURE 15. The logic section shown in FIGURE lS thus will receive as inputs the ~ICA over line 512 ~rom FIGURE 14d, the MCH over line 511 Erom FIGURE
l~e, and the PAL over line 510 from FIGURE 14c. More ' .. .

~ ~6~2~:~

specifically, the output for the pallor volume PAL may ~e applied from FIGURE 14c as input to a multiplier logic section 400 (F~GURE lS) which also receives an input factor -K3 to provide a calculation in form of an output on line 401 leading to the accumulator 403.
Since the pallor volume is a negative value it will be subtracted in the accumulator 403. Likewise, the mean cell areas MCA for the entire population from logic section 310 in FIGURE 14d i5 applied over line 512 as input to a multiplier logic section 405 (FIGURE 15) along with the input factor K2 to provide an output of their product to the accumulator 403. The mean cell hemo~lobin (MCH) provided over line 511 from summation logic 318 (YIGURE 14e) serves an input ot a multiplier logic section ~11 along with a factor K2 to provide an output to the summation logic 403. The output o~ the summation logic 403 is applied over a line 413 to an adder 415 to which is also applied an input factor K4 which is the offset factor. The output Erom the adder ~ logic 415 is the mean cell volume ~or the tota]
population of cells. Typically, the mean cell volume is reported at 430 by printing it out on a form or by displaying it on a cathode ray tube.
In a like manner the computation of mean cell volume (MCV) ~or a subpopulation of cells can be computed or any given subpopulation of cells. For instance, the biconcave cells are separately classified in the example given herein and in the aforesaid application. More specifically, the mean cell area (MCA
1) for the biconcave cells is provided by logic section 288 in FIGURE l~a and this may be applied as an input on line 512 of FIGURE lS. Likewise, the mean cell hemoglobin (MCH 1) for the biconcave cells is provided from logic section 286 tFIGURE 14a) and this may be applied as an input on line 511 of FIGURE lS. The mean .. . ' .

~ 1 B1~71 central pallor volume (PAL) for the biconcave cells is available from logic section 264 in FIGU~E 14c and this may be applied as input over line 510 to the multiplier logic section 400 in the logic shown in FIGURE 15. The respective constants will be applied as K1, K2 and K3 to the respective multiplier logic sections 405, 411 and 400. The remaining operation of the logic section shown in FIGURE 15 will be as above described above to provide an output which is the mean cell volume (MCV) for the biconcave subpopulation o~ cells.
The logic section shown in FIGURE 14d may also be used with various other subpopulations of cells to provide mean cell volume data for various abnormal cell populations. Because such data has not been heretofore available, thPre is a whole new data base ~or diagnosis of cell disorders, blood diseases or of cell morphological changes. For such abnormal cell populations it is preferred to generate for each cell its cell volume, its hemoglobin, and its pallor measurement and then after the cells are classified to accumulate in logic sectlons these values and then determine the mean cell area (MCA), mean cell hemoglobin ~MCH), and mean central pallor volume (P~L). After this, a logic section, such as shown in FIGURE 15 is used to provide a mean cell volume report or output for a given abnormal cell population.
The constants Xl, X2, K3 and K4 are exemplary of the constants which may be used and these constants have been derived or red blood cells which have been spread by a spinner and then dried before image analysis, as above described. The factors tested herein automatically take into account the cell distortion due to drying of the cells prior to examination. On the other hand, 1~ the red blood ells where ~ept wet and analyzed, e.~. while in a liquid stream, the constants 27:~

would be different in order to adjust the mean cell volumes to that achieved by another piece of equipment such as a Coulter Model S counter. Of course, for the MCV of the present invention to accurately correspond to the MCV obtained by a Wintrobe process, the constants would be different as the Coulter counter MCV and the Wintrobe MCV for a given blood specimen will vary.
Whether the Coulter counter or the Wintrobe process gives a more true and more accurate depictation of the MCV is not known. It is clear however, that neither the Coulter nor the Wintrobe processes analyze the mean cell pallor of individual cells, as can be done with the present invention, and use such central pallor data in calculating the MCV for a given red blood cell specimen.
~he factors Kl, K2, K3, and K4 were obtained by using a standard multiple linear regress;on technique as described fully in a publication entitled Nunsrical Method For Scientists and Engineers by R.W. Hamming, published by McGraw Hill in 1962, and by using the mean cel~ volume data for the same blood specimens as had been previously measured with a Coulter Model S
counter. Other techniques such as comparison o~ data obtained emperically, may be used to ~evelop a correlation between the image analysis mean cell volumes ~nd the mean cell volumes obtained with a conventional mean cell volume measuring technique such as the Wintrobe technique or the electrical impedance technique of Coulter.
It is to be recognized that equipment has been developed other than that described herein, which measures by image analysis and which pro~ides a cell size output ln terms such as microcytic, normocytic, or macrocy~ic rather than the pre~erLed size output report o~ a mean cell ~olume measurement. It is understood however that such equipment has not been accurate in ~ ~6~27:~

-~2-that the results obtained were not consistent with the results obtained by conventional equipment which provides an MCV output. The present invention may easily provide such a classification of cells. For instance, the output from logic section 415 may be used to classify cells as microcytic, normocytic or macrocytic by havin~ the output of logic section ~15 applied to a logic section (not shown) having three levels with ~CV below a given level being classified as microcytic, with the MCV's in a central ranye as normocyticr and with the MCV's above an upperlevel of the normocytic range being classified as macrocytic.
On the other hand, the size or volume information already being generated by such equipment may be correlated to that of conventional equipment producing MCV data by using the techniques herein disclosed. Thus, the present invention is not to be construed as being limited to the equipment herein described or to equipment that provides an output only in the terms of mean cell volume (MCV).
In both U.S. Patent 4,097,845 and U.S. Patent ~,199,748 it is p~in~ed out tha~ hard wired logic can be used or that a computer could be used and a specific computer was identified and a long computer program was attached as part of the specification. The computer programs already provided in those disclosures and the information in this disclosure will provide a description sufficient to one skilled in the art to enable the making of a program without undue additional ~ork or experimentation. Hence, the inclusion of another program is submitted not to be warranted and to only result in additional and superfluous material. The present invention li~ewise may be made in hard wired orm without such a computer program and hence the need 3S for a computer program is superf]uous Eor that reason ,. .

~ ~ 6 ~

also~
From the foregoing, it will be seen that the present invention provides a new and improved method and apparatus for generating cell size information correlated with cell size information such as mean cell volumes generated with conventional equipment. ~lthough the cell size information may be repor~ed out as placing the cells in a given size category such as microcytic, normocytic, or macrocytic, it preferably provides a mean cell volume output. Additi.onally, the present invention may be used to provide such size information for a subpopulation of red blood cells

Claims (4)

THE EMBODIMENTS OF THE INVENTION IN WHICH AN EXCLUSIVE
PROPERTY OR PRIVILEGE IS CLAIMED ARE DEFINED AS FOLLOWS:

1. A method of determining the mean cell volume of red blood cells of a particular subpopulation from a blood specimen, comprising the steps of: examining a plurality of red blood cells in the blood, in blood specimen;
classifying individual red blood cells by multiple respective features thereof into a plurality subpopulations; and determining the mean cell volume for a given subpopulation of red blood cells.

2. An apparatus for determining the mean cell volume of red blood cells of a particular subpopulation from a blood specimen, said apparatus comprising: means for examining a plurality of red blood cells in blood specimen;
means for classifying individual blood cells by their individual shape and pallor features into a plurality of subpopulations; and means for determining the mean cell volume for a given subpopulation of red blood cells.

3. A method of determining the mean cell volume of red blood cells from a blood specimen comprising the steps of: examining a plurality of red blood cells in the blood in blood specimen; examining individual blood cells by respective features thereof including central pallors, determining a central pallor measurement indicating the percent of volume indentation due to the central pallor, generating size information for said cells, generating hemoglobin information for said cells, and determining the mean cell volume for a given subpopulation of cells by calculating the volume using said hemoglobin and said size information and deducting the central pallor measurement due to central pallor indentation.

4. A method in accordance with Claim 3, including determining the mean cell area for individual cells and determining the mean cell hemoglobin for individual cells.