This paper presents an effective color normalization method for thin blood film images of peripheral blood specimens. Thin blood film images can easily be separated to foreground (cell) and background (plasma) parts. The color of the plasma region is used to estimate and reduce the differences arising from different illumination conditions. A second stage nor- malization based on the database-gray world algorithm trans- forms the color of the foreground objects to match a reference color character. The quantitative experiments demonstrate the effectiveness of the method and its advantages against two other general purpose color correction methods: simple gray world and Retinex

Dempster, A.G.

Kale, I.

Tek, F.B.

WestminsterResearch

ADAPTIVE GRAYWORLD-BASED COLOR NORMALIZATION OF THIN BLOOD FILMIMAGESF. Boray Tek1* , Andrew G. Dempster2, I˙zzet Kale31 Department of Computer Engineering, Is¸ık University, I˙stanbul, Turkey2School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia3 Applied DSP and VLSI Research Group, University of Westminster, London, UKABSTRACTThis paper presents an effective color normalization methodfor thin blood film images of peripheral blood specimens.Thin blood film images can easily be separated to foreground(cell) and background (plasma) parts. The color of the plasmaregion is used to estimate and reduce the differences arisingfrom different illumination conditions. A second stage nor-malization based on the database-gray world algorithm trans-forms the color of the foreground objects to match a referencecolor character. The quantitative experiments demonstrate theeffectiveness of the method and its advantages against twoother general purpose color correction methods: simple grayworld and Retinex.Index Terms— blood cells, color normalization, grayworld, Retinex, thin blood film,1. INTRODUCTIONMicroscopy examination of blood specimens is still the goldstandard for the diagnosis of several diseases. However, it isusually a tedious and subjective task to perform manually. Onthe other hand, automated diagnosis systems aim to replicatediagnostic expertise by specifically tailored image process-ing, analysis, and pattern recognition algorithms [1]. How-ever, image variations that are commonly neglected by hu-mans generate problems for automated algorithms.An image acquired from a stained blood specimen (thickor thin) using a conventional light microscope can have sev-eral conditions which may affect the observed colors of thecells, plasma (background), and stained objects. These con-ditions may be due to the microscope components such as:different color characteristics of light sources, intensity ad-justments, or color filters; use of different cameras or differ-ent settings in the same camera such as exposure, aperture di-agram, or white balance settings. It is possible to reduce theseeffects by calibration. However, variations can also be causedin slide preparation such as use of different stain concentra-*performed the work while at University of WestminsterCorresponding email: boray.tek@isikun.edu.trtions, exposure to different staining durations, or non-uniformstaining.There are various studies addressing the calibration andcolor constancy issues for general imaging [2]. However, thefield literature for microscope imaging is quite limited. Thegeneral purpose methods of color correction is not appropri-ate for microscope imaging because first the Lambertian sur-face model does not fit to microscope imaging, 2) the use ofthe reference color charts are not practical. Simply, becausethe sensor (or human eye) does not receive the light reflect-ing from a surface; it is the attenuated light that is left fromthe object’s (i.e. specimen’s) absorption. In fact, image for-mation of the stained slides with light microscopes can beappropriately modeled with the “Beer-Lambert Law”, whichstates that there is a linear relationship between the concen-tration, thickness of illuminated media, and the “absorbance”[3]. In addition, the reference color patches (as proposed forother medical imaging applications, e.g. [4], is not practicalfor microscopes. Moreover, there is still the human factor inpreparation of the slides which results in non-standard andinhomogeneous staining concentrations and colors [5].The problem of non-standard preparation of the slides(specimen) was addressed in [6]. To correct under/over stain-ing conditions of the slide, Abe et al. obtained the spectraltransmittance by a multi- spectral camera, and mathemati-cally modeled the relation between the transmittance and theamount of stain (dye) for each pixel using the Beer-LambertLaw and Wiener inverse estimation. However, the methodrequired a multi-spectral camera to operate, and variationscaused by different light sources were not addressed.In [7] we proposed a practical method which exploits thespecial characteristics of the peripheral thin blood film im-ages that are easily separable to foreground and backgroundregions. However, the method was presented briefly as a pro-cedure and no quantifiable evaluation was given. In this study,we reformulate this color normalization algorithm, and com-pare it to two other general purpose color correction algo-rithms: simple gray world [2] and Retinex [8].2. COLOR CONSTANCYThe primary aim of color constancy is to obtain an illumina-tion and imaging sensor independent color representation ofscenes. In general, these two factors are studied separately:illumination change and camera calibration. Sometimes thelatter is not considered (as in this study) when the imagingsensor or camera is unknown or its calibration is not possible.There are some different models of illumination change inthe literature [2]. The “diagonal model” is a simple and satis-factory model which assumes that there is a diagonal 3×3 lin-ear transformation matrix (M) which maps the RGB responseof an unknown illuminant pu = (ru, gu, bu) to the RGB re-sponse of a canonical known illuminant pc = (rc, gc, bc):(pc = Mpu). If the transformation matrix is assumed to bediagonal (1), its non-zero elements (mii) can be calculated bysimple scaling pci/pui where i ∈ {r, g, b}.M = mrr 0 00 mgg 00 0 mbb (1)where the non-zero elements of the diagonal matrix mrr,mgg , and mbb are the illumination factors. If the illumina-tion is assumed to be uniform, using the diagonal model, animage of unknown illumination Iu with RGB color vectorcan be simply transformed to a known illuminant space Icby multiplying all the pixel values with the diagonal matrix(Ic = MIu).The simplest color constancy algorithm is based on the“gray world” assumption that the average value of the sceneis stable (i.e. gray) under the same illumination. Therefore,a deviation of the average value of the image is expected toshow the illumination change. The different interpretationsof this assumption can lead to different approaches. For ex-ample, the average value of the scene can be assumed to bethe level that is a portion (e.g. half) of the maximum possi-ble intensity value of each channel. Thus, using the diagonalmodel, the illuminant factors can be calculated as in (2):mi =Giµui(µui =1N∑NIui ) i = {r, g, b} (2)where Gi is the constant (assumed) gray value and the µui isthe mean of the channel Iui which has N total pixels. Alter-natively, the gray value(s) can be defined with respect to therecorded values of the scene under a known illuminant. Thisis called the database gray- world algorithm [9, 10] and canbe represented as in (3):mi =µciµuii = {r, g, b} (3)Thus the illumination factors are calculated by the ratios ofthe average values of the each channel of the reference (µci )to those of unknown (µui ). Hence the term database refers tocalculation of the gray value from a reference set of images.For ordinary images, the illumination estimation or colornormalization based on the gray world assumption with eitherof the calculations yields poor results compared with moresophisticated algorithms such as gamut mapping, Retinex orcolor by correlation [2, 9, 10]. Obviously, the weakness ofthe gray world algorithm arises from the fact that it is hardto assume that unconstrained physical scenes to average to auniversal gray value.2.1. Proposed MethodologyThe images subject to this study are simpler to generalize be-cause they are composed of two basic parts: plasma and therest which contains mostly red blood cells. Moreover, theseparts can be separated by thresholding. Plasma or more ap-propriately background of the peripheral thin blood film im-age can be assumed to be a colorless transparent region whichreflects the chromaticity of the absorbed light. Therefore,by calculating the average pixel values of the different colorchannels in the plasma (background) region of the images, wecan obtain an estimate for the illuminant RGB, which can beused to normalize each channel (of the whole image) respec-tively.This normalization cannot correct the variation due tostaining because, as explained before, illuminant and stainingare two independent sources of variation. However, now theilluminant variation is saturated, we can assume the perceiveddifference in foreground objects is due to staining concentra-tion. Thus, it is possible to transform foreground pixels usingEq.3, where the gray values under known illuminant can beused as reference or database gray values.Therefore, initially, input images must be separated intoforeground and background regions. The separation (i.e. bi-narization) can be performed using Otsu’s thresholding or themethod proposed by [11], which provides advantages. Thismethod uses area morphology to estimate size of the cells andthen extracts foreground objects and estimates individual his-tograms for the foreground and background regions. Thesehistograms are used to obtain two separate thresholds to per-form a morphological double thresholding operation [12].After separating the input (Iui ) channels (i ∈ {r, g, b}),foreground (Ifui ) and background (Ibui ) images are obtained.Then the proposed color normalization is performed as fol-lows:1. Calculate Mb using (Ibui ) channel averages:mbi =255µIbui.2. Transform the whole image: I1 = Mb Iu using Eq.2.3. Calculate Mf : mfi using Eq. 3 with (If1i ) and the ref-erence image foreground channels If ci .4. Transform only the foreground channels: If 2 = Mf If 15. Replace the foreground channels of I1 with If 2 to ob-tain the final color normalized output image I2.The procedure is demonstrated with an example image inFig 1. Following the image binarization using the area mor-phology technique [11], the background pixels are extracted.Using the background pixel averages in (3), input image isnormalized with respect to the background average color val-ues (estimated illumination color). Then from this image onlythe foreground pixels are extracted and normalized accordingto theR,G,B values that were determined by a reference set.The final result is obtained by replacing the normalized fore-ground pixels.Fig. 1: Color normalization steps: 1) Input RGB image, 2)background pixels are separated, 3) input image is normal-ized by its background average value (for each channelRGB)(I1), 4) foreground pixels are extracted (If 1r,g,b), 5) the fore-ground pixels are normalized If 2, 6) the normalized fore-ground pixels of each channel are replaced with the ones inI1 to obtain output (color corrected image) I2.3. EXPERIMENTAL RESULTSIn order to evaluate the proposed method we have collected 15images of the same thin blood film (standard Giemsa-stained)field using a Brunel Sp 200 (Brunel, UK) microscope anda Canon A60 digital camera coupled with Unilink (Brunel,UK) adapter. In order to construct the database reference(gray) values, a reference set of 12 thin color film images(of different fields) was prepared; however, the images werenot chosen in terms of quality. The only criterion was thatthe images have to be acquired with the same illuminationand camera settings. The illuminant or camera settings werenot recorded or required. After an initial normalization withrespect to the background channel average values R,G,B,the foreground R,G,B channel average values were calcu-lated for each channel (µcr = 183, µcg = 189, µcb = 214),respectively, which were then used in the normalizations ofthe foreground images. To provide a comparison we im-plemented simple gray world normalization algorithm andalso used a MATLABTM implementation of Retinex [8].However, in order to work effectively Retinex implementa-tion [8] requires log-quantized input images, which was notpossible to provide with Canon A60. However, we appliedpre-normalization with respect to the minimum and maxi-mum channel values. The number of iterations parameter forRetinex was set to 4. To measure and compare the perfor-mance, we followed the related works [9, 10] and calculatedthe RMS (root mean square) of the angular difference:Eθ = acos(p1.p2‖p1‖ ‖p2‖ )where p1 and p2 are two different output pixel RGB triples.Fig. 2 shows input images and corresponding out-puts from the three different algorithms: database grayworld (GW-DB), Retinex, and the proposed (foreground-background separation based) gray world (FG-BG GW). Theinput images contain significant color casts which result fromthe different illumination and sensor color balance. In generalall three algorithms can remove the color casts in majorityof cases, whereas our algorithm can recover colors even inthe desaturated image. Figure 3a visualizes the sum of RMSangular differences for each input images, where the RMSdifference values for the unprocessed inputs are provided asa reference. It can be seen that the both of Retinex and FG-BG GW algorithms performed best in five images whereasGW-DB, whereas GW performed poor in most of the cases.Note that, the presented algorithm is not proposed as aniterative process, however it is expected to be idempotent andmake no difference if applied to an input image with cor-rect colors. Figure 3b shows the RMS difference between aninput-output pair of the FG-BG GW algorithm with respectto consecutive applications. It can be observed the significantdifference is obtained in the first run, whereas the algorithmconverges rapidly, as the ratios (i.e. diagonal entries of M)approach to 1. Moreover, we have tested our algorithm on ar-bitrary thin blood film images of different blood abnormalitiesfrom arbitrary internet sources and observed that it providesrobust color correction.4. CONCLUSIONSWe presented a novel color correction method for peripheralthin blood film images based on the gray world and databasegray world assumptions. Our method assumes that plasma(background) is observable in the input image. It furtherassumes the background is colorless under ideal microscopelighting conditions; it scales the whole image to shift theplasma color towards maximum possible level to reduce theeffects of illumination. The foreground color correction as-sumes that the foreground scene in the image is distributednormally around an average value, which is then scaled tomatch a value that is calculated from a reference image set ofdesired color.Fig. 2: Different input images of the same field under dif-ferent imaging conditions and output of gray world (GW),database gray world (GW-DB), Retinex, and proposed grayworld algorithm (FG-BG GW).We show that it is better than the simple gray world al-gorithms, whereas it has similar or better performance toRetinex (in terms of RMS angular error). A color correctionalgorithm must be idempotent if applied to an already nor-malized image. Our algorithm converges rapidly to a stablestate after the first application.In addition, our algorithm is favorable to Retinex, becausethe latter requires iterations which are computationally moredemanding. However, it is possible to investigate whether theproposed method can be improved with use of Retinex insteadof the simple gray world assumption. Moreover, it would beuseful to test and compare the performance of the methodsin terms of their performance as a preprocessing step to anautomatic analysis algorithm.5. REFERENCES[1] F. B. Tek, A. G. Dempster, and A¨zzet Kale, “Parasite de-tection and identification for automated thin blood filmmalaria diagnosis,” Computer Vision and Image Under-standing, vol. 114, no. 1, pp. 21 – 32, 2010.[2] K. Barnard, Practical colour constancy, Ph.d. thesis,Simon Fraser University School of Computing Science,1999.[3] H.-C. Lee, Introduction to Color Imaging Science,Cambridge University Press, UK, 2005.[4] C. Grana, G. Pellacani, and S. Seidenari, “Practicalcolor calibration for dermoscopy, applied to a digitalepiluminescence microscope,” Skin Research and Tech-nology, vol. 11, pp. 242–247, 2005.1 2 3 4 5 6 7 8 9 1000.511.522.533.54ImageRMS Angular ErrorInputGWGW−DBRetinexFG−BG GW(a) 1 3 5 7 901020304050RMS DifferenceIterationRedGreenBlue(b)Fig. 3: (a) Sum of RMS angular differences between colorcorrected outputs of different algorithms, (b) RMS differenceversus iteration of the proposed method for an input and out-put image.[5] Y. Yagi and J. R. Gilbertson, “Digital imaging inpathology: the case for standardization,” Journal ofTelemedicine and Telecare, vol. 11, pp. 109–116, 2005.[6] T. Abe, M. Yamaguchi, Y. Murakami, N. Ohyama, andY. Yagi, “Color correction of pathological images fordifferent staining-condition slides,” in Proc. 6th Inter-national HealthCom Workshop, Odawara, Japan, 2004.[7] F. B. Tek, A. G. Dempster, and I. Kale, “A colournormalization method for giemsa-stained blood cell im-ages,” in Proc. Signal Processing and Applications(IEEE), SIU2006, Antalya, Turkey, 2006.[8] B. Funt, F. Ciurea, and J. McCann, “Retinex in matlab,”in Proceedings of the IS&T/SID Eighth Color ImagingConference:Color Science, Systems and Applications,2000, pp. 112–121.[9] S. Hordley and G. Finlayson, “Re-evaluating colourconstancy algorithms,” in Proc. of the 17th Interna-tional Conference on Pattern Recognition, Cambridge,UK, 2004.[10] K. Barnard, L. Martin, A. Coath, and B. Funt, “A com-parison of computational color constancy algorithms. ii.experiments with image data,” IEEE Transactions onImage Processing, vol. 11, pp. 985–996, 2002.[11] K. N. R. M. Rao, Application of Mathematical Mor-phology to Biomedical Image Processing, Ph.D. thesis,University of Westminster, London, UK, 2004.[12] P. Soille, Morphological Image Analysis, Springer-Verlag, Heidelberg, Germany, 2003.

Adaptive Gray World-Based Color Normalization of Thin Blood Film Images

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Adaptive Gray World-Based Color Normalization of Thin Blood Film Images

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