Standardized color measurement in automated cytophotometry with the light micrscope

(~131 3202/81/040315 09 $02.00/0 Pergamon Press Ltd. © 1981 Pattern Recognition Society

Pattern Reco~lnition Vol. 13. No. 4, pp. 315 323. log1 Printed in Great Britain

STANDARDIZED COLOR MEASUREMENT IN AUTOMATED CYTOPHOTOMETRY WITH THE LIGHT MICROSCOPE A . R U T E R , H. HARMS and H. M. Aus SFB 105 Biomedical linage Processing Research Laboratory, Institute of Virology, Versbacher Str. 7, D-8700 Wiirzburg, Federal Republic of Germany

(Received 14 April 1980; received for publication 22 December 1980) Abstract-This paper discusses the need for and demonstrates the possibility of calibrating a cytophotometry

system using standard colorimetry techniques. In this test, the transmittance of colored sensitometer film strips were measured from 380 to 720 nm in both a spectrophotometer and microscope TV scanning system. From the transmittance data, the CIE chromaticity coordinates of the films were calculated. The colors seen in a light microscope can be correlated to the measurements from a spectrophotometer and calibrated to the tristimulus color system, using standard measurement and analysis methods. Finally, this paper demonstrates an application of colorimetry to the cytophotometric analysis of stained blood cells. Color in cytology

Color detection

Color computation

CIE-DIN standard colorimetry

microscope conditions. The reproducible analysis of stained specimens require both the appropriate computer algorithms, and the calibrations of the cytophotometric system to a colorimetry standard. This paper describes the steps necessary to calibrate an Axiomat light microscope for the analysis of color, based on the tristimulus spectral color standard. The results demonstrate that the colors, seen in a light microscope, can be correlated to spectrophotometry measurements and calibrated to the tristimulus color system, using standard measurement and analysis methods, without a priori knowledge of the application.

INTRODUCTION Computer aided early detection of malignancy has centered mostly on the analysis of cellular size and shape parameters as well as the nucleus/cytoplasm ratio in single cell, monolayer preparations. The more complex and general problem of detecting and classifying malignant and benign conditions in stained blood and bone marrow smears remains, however, a major unsolved challenge in computer-aided cytophotometry. Essential for the differentiation of these conditions is the identification of cells and cellular abnormalities as well as the abnormal occurrences of both normal and pathological cells. A computer decision based on single cells without regard to other criteria such as the surrounding cells is insufficient for an exact diagnosis of leukemias and lymphomas. "9) A combination of the known cytological criteria is always necessary for a diagnostic decision. In addition, for a precise diagnosis of malignancy versus inflammation in the blood system, a series of several different stains may also be necessary. Of the many criteria for cell identification, color is one of the least understood and utilized parameters in computer-aided cytophotometry. Compared to the customary monochromatic analysis, the available polychromatic methods improve both the scene segmentation as well as the subsequent cell identification and classification. HoweveL the techniques are not universally applicable to all cell types~ specimens, stains, staining variations and

PRESENT STATEOF COLOR ANALYSIS IN CYTOPHOTOMETRY

*This work was supported in part by the Deutsche Forschungsgemeinschaft (Sonderforschungsbereich 105 Wiirzburg and Az: Au 55/1), and by the Bundesministerium fiir Forschung und Technologic (Az: 01 VH 056-ZA/NT/MT 225a). Portions of this work are a part of A. Rfiter's thesis.

The least complex of color measurement is achieved with either two, 12-4~, 21 23, 28) three,", a, 13, 14. 31. 35~ or mordT' 1s) narrow band filters. Alternatively, broad band filters, common in color separation work, can also be utilized/11.34) The selection of the suitable filters is based on the spectral characteristics of both the stain and the stained specimen, as well as the visual contrast in the microscope/2s' 29~The colors in the cells are the product of physical and chemical reactions between the chemical stain and the biological substrates. The specimen's color, moreover, usually differs from the original color of the stain's chemical components, The conditions under which similar biological samples always obtain the same color are not fully understood. Despite these difficulties, the currently available color measurement and analysis methods can segment and identify the nuclei, cytoplasm and touching cells in a digitized cell scene. These techniques usually involve

315

316

A. Rf2TER,H. HARMSand H. M. Aus

analyzing the univariate and multivariate histograms from the multiple scanned cell images. (3-5' 20.2~. 2s) Under the fight circumstances, a substantial improvement in the scene segmentation and cell classification can be achieved. The techniques are, however, restricted in their application and the results do not necessarily correlate to the perceived colors in the stained cellular components. On the one hand, the selection of the required filters is usually performed only with respect to the absorption spectrum of the cytological staining solution. On the other hand, the spectrum of the resulting stained cell preparations depends on many additional factors including the fixation and the quality of the staining solution. These variations are normally not considered in the filter selection. Further, there is no consensus in the literature as to which filters and computer processes are to be used for the recognition of color in automated cytology. Color-coded displays of the recorded and processed monochromatic cell images increase the perceived contrast but do not solve the problem of measuring the chromatic information in the specimens. Such methods are also limited by the color spectrum of the monitor. Comparison of the data with other laboratories and reproducible analysis of different preparations require standardized staining methods and calibrated color acquisition systems. STANDARD COLORIMETRY ACCORDING TO THE CIE

In 1931, the Commission Internationale de i'Eclairage (CIE) issued the original recommendations which form the basis of modern colorimetry. These guidelines have been updated periodically by the committee and are summarized elsewhere. (~°'t2) Briefly, the standard observer, the colorimetry coordinate system and several standard illuminants were established by these recommendations. The standard colorimetry observer is defined by sets of color-matching functions (spectral tristimulus values), expressed in terms of spectral stimuli at 700nm (red), 546.1nm (green), and 435.8 nm (blue). The units of the stimuli are adjusted so that the chromaticity coordinates of the equienergy stimulus are all equal (Fig. 1). All colours in the visual spectrum can be reproduced by these three primary stimuli. The other color-matching functions involve linear transforms of these spectral tristimulus primaries. The CIE guidelines define the spectral tristimulus values at every nanometer from 380nm to 780nm. An abridged set of tristimulus values is also defined at 5 - 1 0 n m intervals over the same visual range. Since the primary stimuli are based on the standard observer, the measured chromaticity and lightness coordinates can be directly correlated to the physiological color perception. Calculation of the chromaticity and lightness coordinates requires measuring the spectrum from 380 to 780nm in 1-20 nm intervals, depending upon the accuracy required. Since this is time consuming and expensive,

methods have also been developed to directly measure the tristimulus values with three appropriately designed optical filters. Before proceeding to the spectral measurement used in this paper, a brief review of this measurement technique is presented below. The current research activities in colorimetry are described in the proceedings of the International Colour Association. (s' 9) DIRECT MEASUREMENT OF THE TRISTIMULUS VALUES

Theoretically, the three primary stimuli in Fig. 1 of any color in the visual spectrum can be measured by three appropriately designed optical filters. The most common example of such a trichromatic color matching system is color television. The design of the filters for such measurements must satisfy the Luther condition by compensating for the non-ideal characteristics of the light source, the optics and the photo detectors. (27) The required compensation is difficult and must be continuously adjusted because the characteristics of all light sources and photoconductors vary with time and temperature. Three-filter methods do not provide spectral information about the color, nor can the measured values always be transformed to other color coordinate systems. The scene to be analysed by the computer method is digitized through each of these three filters. The differences in the three resulting achromatic images depend upon the colors in the original scene. But none of the photoelectric filter colorimeters presently available on the market incorporates spectral response functions in perfect agreement with the tl(2), t2(2), t3(2)functions. This deficiency limits the usefulness of these instruments for reading the CIE tristimulus values T~ (i = 1, 2, 3) directly. (33~ The current multispectral methods exploit these differences to extract contours and features from the scanned scene. The digital trichromatic scene segmentation methods pioneered by Ohlander, (24) Price, (26) Kender (~7) and Tenenbaum (3°) are difficult to reproduce under other illumination and measurement conditions, especially in cytophotometry.

s

t2

_~

o> ~o

t3

I'~- 0 5

,

400

500 600 Wovelength h , nrn

700

Fig. 1. CIE tristimulus values as a function of wavelength in the visual spectrum, tl is blue, tz is green, and t 3 is red.

Standardized color measurement in automated cytophotometry Since

T H E SPECTRAL M E A S U R E M E N T O F T H E T R I S T I M U L U S PRIMARIES

3

Scanning the polychromatic images at 1-20nm intervals from 380 to 780nm provides the spectral information which depends least on the light source, the image sensors and the measurement conditions. F o r practical reasons, the scan interval is usually 10-20 nm. Within the limits imposed by the characteristics of the measurement system, the spectral data acquired by this method can be systematically corrected. In addition, the spectral data can be transformed into any arbitrary color coordinate system. The general form of this transformation is : 720

Tj = k

C

nm

s().)T,j(2)fl().)d2

J 380

j = 1,2,3

(1)

am

where fl(2) is the measure transmittance corrected according to measurement system characteristics, T,j(~) is the spectral tristimulus values fundamental to the definition of the primary system, s(2) is the relative spectral power distribution of the utilized standard illuminant and k is a normalizing factor. The weighting function s(),) and T,~(),) are obtained from the CIE reference tables. (~°~ Since the visual spectrum is scanned in discrete intervals in a spectrophotometer, the integral equation (1) can be replaced by a summation "]20 n m

Ts = k

s(7.,) T,s(2,)/7().,) A).,

~ 380

(2)

nm

where A),~ = 5 nm. fl(),D is read directly from the spectrophotometer. For the Axiomat TV measurement system with the 24 available narrow band filters, equation (1) can be rewritten as: 2,t

Tj = k ~, s()+i)T~s().i)fl().i) A21

(3)

i=l

where A).i =

(filter

i +

1) -

(filter i - 1)

2

and A;,I = A224 = 20nm. The corrected measured density fl().~) for the TV scanner on the Axiomat is determined by

fl(}.,)

mVi" TWi TVi" WVi

=

(4)

where mVi is the measured light intensity with filter i, WV~ is the calculated average white reference with filter i. TV~ is the transmittance of the gray wedge when measuring the film strip, TW~ is the transmittance of the gray wedge when measuring the white reference. The chromaticity coordinates, which are normalized tristimulus values, are defined by:

/

s i Ts

)

/=1,2,3.

317

ti = 1, i=l

only two of the chromaticity coordinates are required to describe the hue and saturation. Hue is the attribute of a color perception denoted in nanometers. Saturation is the degree of difference in the color from the achromatic white reference point. The higher the rate of change in the spectral transmittance, the greater is the saturation of the perceived color. The third dimension is, in this case the color's lightness, since non-self luminous objects are being measured/a2~

The measurement For these calibration measurements, the six colors of an Agfachrome sensitometer test strip were used as reference material. The test strips contain 6 x 6 mm 2 areas of the basic colors : magenta, red, yellow, green, cyan and blue. First, these strips were measured in a spectrophotometer (PMQ2, C. Zeiss, Oberkochen) at 5 nm intervals from 380 nm to 720 rim. The same color film strips were subsequently mounted on a black cardboard mask and inserted into the microscope light beam behind the magnifying objective. "6~ The location of the film in the beam was selected so as to minimize the unwanted side effects of the film grain. For each color, a 60 x 60 pixel area of the film was scanned with the 24 available interference filters. The filters are spaced at 10-20 nm intervals from 378 nm to 741 nm. The white reference measurements, needed for the correction factors, were obtained by repeating the 24 scans without any film in the light beam. The light source was a Xenon 150 W lamp (Osram, Miinchen). Since all the results are normalized with respect to the white reference measurements, the type of illuminant, s(,;~l), does not appear in equation (4). Consequently, any of the standard illuminants can be chosen for equation (3). The investigation reported here used the ideal, equal-energy stimulus E. The transmission characteristics of the 'neutral' density sliding gray wedge was also measured in the PMQ2. The grey wedge is used to limit the incident light on the TV camera in the Axiomat scanning system. From these measurements, in both the P M Q 2 and the Axiomat, the spectral curves and the chromaticity coordinates of the six films were calculated. Using the spectral curves from the P M Q 2 as standard, the green, blue, cyan and yellow were normalized at 555 nm, and the magenta and red at 640 nm, for easier comparison. SPECTRAL TRANSMITTANCE, L I G H T N E S S AND C H R O M A T I C I T Y C O O R D I N A T E S OF T H E SIX MEASURED COLORS

The spectral transmittance curves, fl(;-i), of the six colored film strips, measured in the PMQ2 and the Axiomat TV scanner, are plotted pairwise in Fig. 2. The transmittance curves for the blue and red film strips, measured in both systems agree in the wave-

318

A. ROTER, H. HARMSand H. M. Aus

length range from 500 to 741 nm and 380 to 670 nm, respectively [Fig. 2(a)]. The green curve from the microscope has a smaller peak at approximately 540 nm and, consequently, a lower rate of change than the P M Q 2 data [Fig. 2(b)]. The agreement of the magenta data, from the two measurements, is limited to the interval from 450 to 670 nm [Fig. 2(b)]. Cyan, like blue, agrees from 450 to 741 nm, whereas the yellow curves are similar between 480 and 560nm, only [Fig. 2(c)]. The correction factors in equation (4) had the greatest effect on the data from the yellow film, because yellow is the lightest of all the six colors used in this experiment and resulted in the largest measured

transmittances. The added correction factors for the neutral density gray wedge are necessary because the measurements in the PMQ2 spectrophotometer have shown that the 'neutral' density gray wedge varies throughout the visual spectrum from a maximum of 0.542 at 421 nm to 0.024 at 721 nm. The chromaticity coordinates (tt, t2) of all six color film strips are plotted in the CIE color diagram (Fig. 3). The flatter peak and the associated lower rate of change in the measured green transmittance data [Fig. 2(b)] results in the lower green saturation in Fig. 3. The larger derivatives of the TV transmittance data for both magenta [Fig. 2(c)] and red [Fig. 2(a)] in the

0.3

/

8 ~oz

"'-.--

c

.o O=

I,-

Oq 378 4 0 0

] ] ]

~ m

600

700

7'9,1

Waveleng'l'h • nm

•

(b)

03

•

"c

5

0 378 400

500

600

700

741

Wavelength ~ nm 06 ~

o

8

03

='~

/

8

378 4 0 0

500

600

700

741

Woveleng'rh ,, nm

Fig. 2. Transmittance curves of the six Agfa sensitometer film strips measured in a spectrophotometer (O, []) and in the Axiomat TV scanner (Q, 1). (a) Red (O, O) and blue (I-1, II). (b) Green (O, Q) and magenta ([-], I ) . (c) Yellow (O, Q) and cyan (D, 1).

Standardized color measurement in automated cytophotometry CIE

319

Chromaticity diagram 52O

08

06

;oo

04

Green

~ \

~57o

"~°° +E

.X~,o

02

×

Standard illuminant E

x

: 0.3333

y : 0.3333

Fig. 3. Calculated chromaticity coordinates of the six Agfa sensitometer film strips from the spectral information in Fig. 2. The circles ( 0 ) are from the spectrophotometer. The squares (Fq) indicates the ranges of the calculated values from the TV scanner measurements, with the film grains out of focus. Calculated chromaticity coordinate regions of the pixels from nuclei (///) and nucleoli (\~)(nucleus 1 left).

interval from 680 to 741 nm, produce the apparently more saturated chromaticity coordinates shown for these colors in Fig. 3. The shift of the cyan color towards the blue in Fig. 3, corresponds to the shift in the transmittance peak, at approximately 450nm, towards the ultra-violet (Fig. 2). The calculated lightness of the films, ranked in increasing order are : red = 3.753; blue = 5.319; magenta = 6.917; green = 8.277; cyan = 17.219; yellow = 41.074. The location of the film in the microscope light beam greatly influenced the range of the calculated chromaticity coordinates. For example, with the film grain visible in the TV monitor, the range in yellow was At1 = 0.0563 and At2 = 0.0445 ; whereas, with the film grain out of focus the range was only At1 = 0.0042, At2 = 0.0040 (Fig. 3). Measuring precisely the same area of the film in both the P M Q 2 and the Axiomat TV scanner was technically not possible.

S p e c t r a l m e a s u r e m e n t s in a b l o o d s m e a r

Two nuclei (Fig. 4) from a blood smear stained with purified Azur B-Eosin were scanned with the same 24 filters and lamp described above. Both nuclei exhibited at least one nucleolus [arrows, Fig. 4(a, b)] and are located within close proximity to each other on the glass slide (approximately 23/am). Note the visual differences between the same cells photographed at

two different wavelengths in Fig. 4. In Fig. 4(c) the nucleolus is clearly visible, whereas in Fig. 4(d) the nucleoli are barely distinguishable from the nucleus. Nucleus 2 [Fig. 4(b)] is also paler blue than the nucleus in ['Fig. 4(a)-]. The spectrum was measured and calculated for each pixel in the nucleus as well as in the nucleoli. The nucleoli were interaetively segmented from the nuclei. The average spectral transmittance curves are shown in Fig. 5. The visual impressions in the microscope are confirmed I~y the measured spectral differences in Fig. 5(a, b). The ~aucleolus in Fig. 4(a) is considerably bluer (380-460nm) than the nucleoli of Fig. 4(b). Conversely, the latter are redder (510-680nm) than the former [Fig. 5(a)]. Similarly, the nucleus of Fig. 4(a) is bluer and the corresponding transmission values in the range 460 to 780nm [Fig. 5(b)] are smaller than those from nucleus 2. The spectra of the nucleoli with the corresponding nuclei are plotted in Fig. 5(c) and (d). Comparing both nucleoli spectra to those of their corresponding nuclei, it can be observed that both exhibit similar responses relative to their respective nuclei. In both cases the nucleoli are brighter than the nucleus in the range from 480-560nm and darker than the nucleus from 560 to 700 nm. The calculated chromaticity coordinate regions from the nuclei and nucleoli are also plotted in Fig. 3.

320

A. ROTER,H. HARMSand H. M. Aus

A N2 N|

C

Fig. 4. (a, b) Cell nuclei from a purified Azur B-Eosin stained blood smear photographed in the TV monitor at 540rim. (c, d) same nuclei as in (a) and (b), respectively, photographed at 600nm.

COMMENTS The reported results demonstrate the possibility of calibrating a cytophotometry system with the CIE colorimetry standards. The data obtained with both measurement systems reveals a generally good correlation in the portion of the visible spectrum where the sensitivity of the TV scanner is adequate. The data from the yellow film demonstrates the close correlation of the chromaticity coordinates despite the differences in the two spectral transmittances. The deviations in the transmittances measured in the Axiomat can be explained by the insufficient light sensitivity of the Vidicon TV scanner in both the blue and the red portions of the visual spectrum. The newly developed Chalnicon®* tube with its increased light sensitivity, especially in red, promises to substantially reduce this major deficiency in the cytophotometric measurement system. The problem posed by the grains in the film can be eliminated by using color glass mounted in a revolver plug-in unit now available for *Registered trade mark of Toshiba, Tokyo Shibaura Electric Company, Ltd. Tokyo, Japan. Mention of a product trade mark does not imply endorsement.

the Axiomat. Visual observation of the two cells in the light microscope revealed no obvious similarities between the two nuclei. Nucleus 1 was strongly granulated and a deep bluish magenta color compared to the reddish magenta appearance of nucleus 2. The nucleolus in the first cell exhibited a relatively deep color between violet and magenta, the nucleoli in the second cell were a light shade of blue. Compared to the plasma of the respective nuclei, the colors of the nucleoli were relatively homogeneous. Both this highly subjective description of the colors seen in these two cells and the transmission spectra shown in Fig. 5 suggest that all three CIE parameters--saturation, lightness and h u e - - v a r y with the amount of stain uptake in the cell."5~ Both hue and saturation are different in the nuclei and nucleoli piottqd in Fig. 3. The hue of nucleus 2 and the associated nucleoli is toward the magenta region of the color triangle, whereas the hue of the nucleus 1 and the associated nucleoli is more in the blue region. Nucleus 1 is also more saturated than nucleus 2. Despite these rather large variations, both the spectra (Fig. 5) and the chromaticity coordinates (Fig. 3) of the nucleoli, relative to the respective nuclei, are quite similar.

Standardized color measurement in automated cytophotometry ]500 F-

321

1500

(bJ

(o) \

I000

--

Nucle,

///

\\

g ~oo

Ofllli 37J

I I I t ~ k I 11 I I I I I I I I I I t I I .L_LLI [ I I I.LLLI 4O0

500

600

Wovelenl~'h,

%

700

LLJllllltill[llllltLlllLlllilltlll aoo 50O

37

750

600

Wovelength,

nm

7OO

~1 750

nm

(d

f

Nucleus

I

~

'r--

Nucleus 2

,ooo I

E

6i, 57,

,I ~l III 400

IIJ_LLLLL;Lll 500

Wovelengtn,

I I I I I t I I t I I El 1]111 60O 7O0 750 nm

3

LIlilll 71 ~ 0 0

11[111 50O

Ill

Wavelength,

I Ijlll~ull 600

I till 700

tl ~ 750

nm

Fig. 5. Average spectral transmittance of the nucleoli (a) and nuclei (b) in Fig. 4. Nucleus 1, ; Nucleus 2, - . . . . . Mean spectral transmission of the nucleoli and the corresponding nuclei for (c) nucleus 1 and (d) nucleus 2 in Fig. 4 (nuclei . . . . , nucleoli -).

Calibrating the cytophotometric measurement system with the CIE colorimetry recommendations has the distinct advantage of providing for the quantitative measurement of the subjective color perception and the correlation of these measured results with standardized color charts for all the naturally occurring colors. A fundamental advantage of the CIE colorimetry methods, compared to the commonly collected polychroraatic information in cytophotometry, is that all the obtained results are relative to the measured white point.

SUMMARY Of the many criteria for cell identification, color is one of the least understood and utilized parameters in computer-aided cytophotometry. Compared to customary monochromatic analysis, the available polychromatic methods improve both the scene segmentation as well as the subsequent cell identification and classification. However, the techniques are not universally applicable to all cell types, specimens, stains, staining variations and microscope conditions. The reproducible analysis of stained specimens requires both the appropriate computer algorithms, and the calibration of the cytophotometric system to a colorimetry standard. This paper describes the steps necessary to calibrate an Axiomat light microscope for

the analysis of color based on the tristimulus spectral color standards. The topics discussed include present state of color analysis in cytophotometry, standard colorimetry according to the CIE, direct measurement of the tristimulus values, the spectral measurement of the tristimulus primaries. In this test, the transmittance of colored sensitometer film strips were measured from 380 to 720nm in both a spectrophotometer and microscope TV scanning system. From the transmittance data, the CIE chromaticity coordinates of the films were calculated. The data from both measurement systems reveal a generally good correlation to each other in the portion of the visible spectrum where the sensitivity of the TV scanner is adequate. Calibrating the cytophotometric measurement system with the CIE colorimetry recommendations has the distinct advantage of providing for the quantitative measurement of the subjective color perception and the correlation of these measured results with standardized color charts for all the naturally occurring colors.

Acknow!eth]ements--Theauthors wish to thank M. Haucke, F. Meinl (C. Zeiss, Miinchen), H. Loof (C. Zeiss, Oberkochen) and R. Schl~ifer(Jenaer Glaswerk Schott & Gen., Mainz) for their technical assistance. The purified Azur B-Eosin was obtained from Prof. Dr. reed. D. Wittekind, Anatomisches Institut der UniversiQit Freiburg, Germany. The manuscript was typed by H. Schneider and S. Troll.

32'2

1. 2. 3.

4.

5. 6.

7.

8. 9.

10. 11. 12. 13. 14. 15. 16. 17.

A. RUTER, H. HARMS and H. M. Aus REFERENCES R. K. Aggarwal and J. W. Bacus, A multispectral approach for scene analysis of cervical cytology smears, J. Histochem. Cytochem. 25, 668-680 (1977). H. M. Aus, U. Gunzer and V. ter Meulen, A note on the usefulness of multi-color scanning and image processing in cell biology. Microscope 24, 39-44 (1976). H. M. Aus, A. Riiter, V. ter Meulen, U. Gunzer and R. Niirnberger, Bone marrow cell scene segmentation by computer-aided color cytophotometry, J. Histochem. Cytochem. 25, 662-667 (1977). J. W. Bacus, An automated classification on the peripheral blood leukocytes by means of digital image processing. Ph.D. Thesis, University of Illinois Medical Center (1971). J. W. Bacus, A whitening transformation for two-color blood cell images, Pattern Recognition g, 56-60 (1976). J. F. Brenner, E. S. Gelsema, T. F. Necheles, P. W. Neurath, W. D. Selles and E. Vastola, Automated classification of normal and abnormal leukocytes, J. Histochem. Cytochem. 22, 697-706 (1974). J. F. Brenner, B. S. Dew, J. B. Horton, Th. King, P. W, Neurath and W. D. Sclles, An automated microscope for cytologic research - - a preliminary evaluation, J. Histochem. Cytochem. 24, 100-111 (1976). D. B. Judd, ed., "Colour 7Y, Proceedings of the Second Congress of the International Colour Association, University of New York. A. Hilger, London (1973). F. W. Billmeyer Jr. and G. Wyszecki, eds., 'Colour 77', Proceedings of the Third Congress of the International Colour Association. Troy, New York. A. Hilger, Bristol (1977). 'Colorimetry', Publication CIE No. 15 (E-1.3.1) (1971). B. Dew, Th. King and D. Mighdoll, An automatic microscope for differential leukocyte counting, J. Histochem. Cytochem. 22, 685-696 (1974). DIN Normblatt 5033, Teil 1-9: 'Farbmessung'. J. E. Green, A practical application of computer pattern recognition research - - the Abbott ADC-500 differential classifier, J. Histochem. Cytochem. 27, 160-173 (1979). J. E. Green, Rapid analysis of hematology image data - the ADC-500 processor, J. Histochem. Cytochem. 27, 174-179 (1979). F. Habermalz, Farbmetrische Untersuchung yon mikroskopischen Farbstoffen und F/irbungen, Microsc. Acta 80, 199-205 (1978). H. Harms, A. Riiter and H. M. Aus, A microprocessor controlled Axiomat microscope for acquisition of cell images, Pattern Recognition 13, 325-329 (1981). J. R. Kender, Saturation, hue and normalized color: calculation, digitization effects and use. Department of Computer Science, Carnegie-Mellon University, Pittsburgh (1976).

18. A. v. Kulkarni, Effectiveness of feature groups for automated pairwise leukocyte class discrimination, J. Histochem. Cytochem. 27, 210-216 (1979). 19. K. Lennert, Malignant Lymphomas other than Hodgkin's disease, pp. 93-106. Springer, Berlin (1978). 20. H. P. Mansberg, A. M. Saunders and W. Groner, The Hemalog D white cell differential system, J. Histochem. Cytochem. 22, 711-724 (1974). 21. J.K. Mui, J. W. Bacus and K. S. Fu, A scene segmentation technique for microscopic cell images, Proceedings of the Symposium on Computer-Aided Diagrams of Medical Images S. Sklansky, ed. IEEE, San Diego, CA (1976). 22. J. K. Mui, K. S. Fu and J. W. Bacus, Automated classification of blood cell neutrophils, d. Histochem. Cytochem. 25, 633-640 (1977). 23. J. K. Mui and K. S. Fu, Feature selection in automated classification of blood cell neutrophils. I EEE CH 1318-5 (1978). 24. R. Ohlander, K. Price and D. R. Reddy, Picture segmentation using a recursive region splitting method, Comp. Graphics Image Process. 8, 313-333 (1978). 25. L. Ornstein and H. R. Ansley, Spectral matching of classical cytochemistry to automated cytology, J. Histochem. Cytochem. 22, 453-469 (1974). 26. K. E. Price, Change detection and analysis in multispectral images. Department of Computer Science, Carnegie-Mellon University (1976). 27. M. Richter, Einfiihrung in die Farbmetrik, Sammlung G6schen, p. 133. Walter de Gruyter, Berlin (1976). 28. A. Riiter, H. Harms, M. Hauck¢ and H. M. Aus, Digital¢ Auswertung der Farbinformationen yon lichtmikroskopischen Zelibildern, Informatik Fachberichte 17, E. Triendl, ed., pp. 311-317. Springer, Berlin (1978). 29. A. Ruthmann, Methods in Cell Research, p. 220. G. Bell & Sons, London (1970). 30. J. M. Tenenbaum, T. D. Garvey, S. Weyl and H. C. Wolf, An interactive facility for scene analysis research. Artificial Intelligence Center Technical Note No. 87. Stanford Research Institute (1974). 31. D. H. Tycko, S. Ambalagan, H. C. Liu and L. Ornstein, Automatic leukocyte classification using cytochemically stained smears, J. Histochem. Cytochem. 24, 178-194 (1976). 32. G. Wyszecki and W. S. Stiles, Color Science - - Concepts and Methods, Quantitative Data and Formulas, p. 229. John Wiley & Sons, New York (1967). 33. G. Wyszecki, Colorimetry, Handbook of Optics, W. (3. Driscotl and W. Vaughan, eds., pp. 9-36. McGraw-Hill, New York (1978). 34. I.T. Young, The classification of white blood cells, IEEE Trans. Biomed. Engng 19, 291-298 (1972). 35. I. T. Young and I. L. Paskowitz, Localization of cellular structures, IEEE Trans. Biomed. Engng 22, 35-40 (1975).

About the AUtbor-ARND RI~ITER received the Dipl.-Math. degree in mathematics/informatics from the Technical University Dresden in 1972. From 1972 to 1975 he was in charge at the Robotron computer production in Dresden. Since 1976 he has been a staff member in the SFB 105 Biomedical Image Processing Research Laboratory at the University of Wiirzburg. His special interests center around the high resolution detection and analysing ofcolor in cell preparations due to the varying staining and fixation conditions. He is currently working on his doctoral dissertation at the University of Bremen. About the Author-HARRY HARMSreceived the lng.(grad) degree in Electronics from the FHS Aalen in 1972 and the Dipl.-Ing. degree in Electrotechnics and Cybernetics from the University of Bremen in 1976. Since 1977 he has been a member of the Biomedical Image Processing Laboratory at the University of Wiirzburg. His special interests are microprocessors, autofocus algorithms and texture analysis. He is presently working on his doctoral dissertation at the University of Bremen.

Standardized color measurement in automated cytophotometry

323

About the Aathor--H. M. Aus received the B.S. degree in 1963 from the University of Minnesota, the M.S. degree in 1967 from the University of New Mexico and the Ph.D. degree in 1971 from tl-,e University of Arizona. He was a staff member of the Sandia Corporation, Albuquerque from 1964 to 1967, underwent NASA Traineeship from 1967 to 1970 and was a research assistant from 1970 to 1971 at the Department of Electrical Engineering, University of Arizona. He was a faculty member of the Department of Electrical Engineering, Colorado State University from 1971 to 1972 and a postdoctoral fellow at the Max-PlanckInstitute, O6ttingen, from 1972 to 1975. Since 1976 he has been a research associate at the Institute of Virology, University of W~rzburg.