The goal of this paper is to present how two different image processing approaches can be enhanced by merging both methodologies. We will see how the results of a perceptual labelled segmentation methodology [7] can be improved by applying a fuzzy classification algorithm [2] based on a fuzzy outranking methodology [9] as a postprocessing algorithm, and viceversa. A comparison of the individual algorithms with the combination of both algorithms will be presented in order to demonstrate the improvement. Color Bone Marrow (1) images will be used. The objective is to detect White Blood Cells. The detection of white blood cells in bone marrow microscopic images presents big difficulties because of the great variance in their characteristics and also because of staining and illumination inconsistences. On the other hand, the maturity classes of white blood cells actually represents a continuum; cells frequently overlap each other, and there is a fairly wide variation in size and shape of nucleus and cytoplasm regions within given cell classes

Amo, Ana del

Montero, Javier

Montseny, E.

Sobrevilla, P.

English

Annual Meeting of the North-American-Fuzzy-Information-Processing-Society
JUN 27-30, 2004The goal of this paper is to present how two different image processing approaches can be enhanced by merging both methodologies. We will see how the results of a perceptual labelled segmentation methodology [7] can be improved by applying a fuzzy classification algorithm [2] based on a fuzzy outranking methodology [9] as a postprocessing algorithm, and viceversa. A comparison of the individual algorithms with the combination of both algorithms will be presented in order to demonstrate the improvement. Color Bone Marrow (1) images will be used. The objective is to detect White Blood Cells. The detection of white blood cells in bone marrow microscopic images presents big difficulties because of the great variance in their characteristics and also because of staining and illumination inconsistences. On the other hand, the maturity classes of white blood cells actually represents a continuum; cells frequently overlap each other, and there is a fairly wide variation in size and shape of nucleus and cytoplasm regions within given cell classes.Depto. de Estadística e Investigación OperativaFac. de Ciencias MatemáticasTRUEpu

Docta Complutense

Fuzzy Classification Improvement by a Pre-Perceptual Labelled Segmentation Algorithm Ana Del Amo Smiths Aerospace ES- G R  3290 Patterson Avenue, SE Grand Rapids, MI  49512-1991, USA ana. del. amo@smiths-aerospace. com Pilar Sobrevilla and Eduard Montseny Technical University of Catalonia, Barcelona, Spain eduard.montseny@upc. es pilar.sobrevilla@upc. es Javier Montero Mathematics Faculty Complutense University, Madrid, Spain monty @mat.ucm.es Abstract The goal of this paper is  to present how two different image processing approaches can be enhanced by merging both methodologies. W e  will see how the results of a perceptual labelled segmentation methodology (7' can be improved by  applying a fuzzy classification algorithm [2] based on a fuzzy outmnlcing methodology [g] as a postprocessing algorithm, and viceversa. A comparison of the individual algorithms with the combination of both algorithms will be pre- sented in order to demonstrate the impruve- ment. Color Bone Marrow images will be used. The objective is to detect White Blood Cells. The detection of white blood cells in bone marrow microscopic images presents big d i f i -  culties because of the great variance in their characteristics and also because of staining and illumination inconsistences. On the other hand, the maturity classes of white blood cells actually represents a wntinuum; cells fwquently overlap each other, and there is a fairly wide variation in size and shape of nucleus and cytoplasm re- gions within given cell classes. 'The images are from a database collected at the Ellis Fischel Cancer Center, University of Missouri 1 Introduction Image segmentation is the process of partitioning an image into meaningful regions that correspond to ob- jects within the scene. The partitioning process usually stops when all regions of interest have been identified. This is easy to achieve in certain controlled applica- tions but not that easy when the outcomes are un- known. Segmentation has a unique position within a machine vision system since it can be said that it forms the bridge between low-level and high-level processing operations. Image classification is an extremely complex prob- lem and accuracy is a very important issue. The simplest form of digital image classification is to con- sider each pixel individually, assigning each to a class based upon several values measured in separate spec- tral hands. This type of classifier is usually referred to as a spectral or point classifier. We will apply an un- supervised fuzzy classification algorithm designed as a multicriteria decision-making problem based on fuzzy set theory. The combination of both approaches will result in an improvement for automatic detection. The classi- fication system we are proposing here would have an initial preprocessing part were an RGB to Smith's HSI transformation [13] will be performed before a pixel by pixel classification. 486 0-7803-8376-1/04/$20.00 Copyr igh t  2004 IEEE. 2 Selection of color patterns The pattern’s colors are to be characterized by their hue and saturation and should be distributed within the H-S map in such a way that adjacent regions should have significantly different values with respect to the characteristics on which they are uniform. Five sat- uration subintervals labelled Very Low -VL-, Low &, Medium -M-, High -Hi-, and Very High - m i -  are being used. Fifty-three colors arranged as one color belong- ing to the first subinterval, four within the second one, and the other forty eight colors distributed among the Medium, High and Very High subintervals are consid- ered. Once the pattern colors Ck have been selected, the system will characterize their chromatic by means of fuzzy sets pc, {l 5 k 5 53). Two membership functions (&, p Z k )  will depict the color pattern typ icality of both chromatic components, and would he aggregated using the geometric mean in the expression below in order to define the pattern color’s membership function: P C ~  (Pij) = 4- (1) According to Robertson ill],  perceptual variables hue, saturation and lightness are related to the psychophys- ical variables dominant wavelength, dye, purity and lu- minance, respectively. Perceptual variables are depen- dent on the perceived environment since, for the same color stimuli, their values may vary with surround- ing light changes. In order to represent human color perception, a transformation from RGB to  HSI (Hue- Saturation-Intensity) has to be performed. Therefore, a color-coordinate system in compliance with human color perception is obtained. There is a plethora of these transformations in Computer Vision references ([lo], [ l l] ,  and [15]) with different notations (HSV, HSB, IHS, HIS, etc.). However, the general idea is basically the same in all of them, i.e., one component for the Lightness value (I, V) and two other compc- nents for the Hue and Saturation of the color. Af- ter a research on the behavior of some HSI spaces under different conditions of illumination and noise, we have chosen the Smith’s HSI space [13]. In this model, components are represented in a perfect cylin- der, and transformation from RGB to HSI is provided by linear equations, which makes the calculus easier and reduces the computational cost. Besides showing the maximum component reliability under illumination variations, the Smith’s components present three clear advantages with respect to RGB components: 1. HSI provides high independence between their color components. 2. HSI codes human meaningful information (color dye, chromaticity degree, etc.). 3. HSI allows more efficient isolation to physical illu- mination phenomenon (shades, highlights, etc.). 3 Stability-Based Hue and Saturation Color Model After studying the behavior of Hue and Saturation Smiths’ components, under different illuminance condi- tions, their deviation predictors EH and Es have been defined by equations (2) and (3), respectively. In these expressions I and S are the intensity and saturation values of the considered pixel. 20000 EH = -I X S  500 I Es = - (3) These predictors provide a degree of confidence of any H-S pair, and allow approximating color components deviations for any particular (individual) pixel, which will be particularly useful within the pixel classification process, considering components’ variability. Based on deviation predictors and considering that the bigger the deviation, the smaller the stability, Hue and Sat- uration stability functions, noted by FH and Fs, were introduced in [l2] by (4) and (5) respectively. These functions allow assuming the variability of the chrc- matic components, and will be used for obtaining the similarity degrees of color image pixels and system col- ors. kH FH = min { %, I} FS = min {%,I} k s  (4) (5) The scalar factors kx and ks appearing within previous formula can be chosen to suitably fit red data degree of variance. 4 Fuzzy Classification Algorithm In this section, we will present a schema of the al- gorithm in general terms. The algorithm follows three steps: a.- b.- C: RGB to Smith’s HSI transformation. Color patterns membership degrees computation. Pixel by pixel image classification. 487 Step (a) follows the approach presented in [13] to t rans  form the RGB values into Smith’s HSI. Step (b) con- siders the Hue and Saturation stability functions ( (2) ,  (3)) in the final Hue and Saturation membership func- tion computation as follows: &(pij) = p g k ( h  - (hk - hij) x F ~ ( p i j ) )  (6) & ( ~ i j )  = & (sk - (sk - sij) x Fs(pij))  (7) They represent the similarity degrees of Hue and Sat- uration values of pixel p i j .  hij and sij are the Hue and Saturation values of pixel p i j ,  h k  and sk are the central values of the corresponding Hue and Saturation membership functions, and Fn(p i j )  and Fs(p,) are the Hue and Saturation stability values for pixel p i j .  Each pixel p i j  in step (c) is then labelled according to the following rules: (8) Where pck has been obtained as established by (1). Once the perceptual labelled segmentation has been accomplished, we apply a fuzzy point classifier. Let x = { X i j / i  = 1 , .  .. , r  j = 1, .. , ,s) be the image set to be processed2. Let (51 xz. . . . , zn-) E E’’ be the vectorial represen- tation of a collection of features for element X i j ,  in which xij is the value of feature f for element Xi j .  We are also considering the expert knowledge, so, we are aware of the existence of three main different kinds of objects in the image. Therefore, three fuzzy classes will be considered. For each one of the classes, in which the classification will be performed, a range of valid values has to be defined. The membership function for each class Ck with respect to each f property is defined following [2]. Each object Xij  has an associated vector each class C,, which shows the different degrees of verification each property has with respect to each class. The classification problem is at this point, solved as a multicriteria decision making problem following an outranking methodology. Each one of the pixels in the pattern image will have a representation as follows: bYj(Ck) = . b, j (Ck)  as the membership degree to class C,. Where &;”(Ck) is the fuzzy set of non-dominated classes representing the ‘X, represent each one of the already labelled pixel fict (pi)) tj’ t) 7 ‘3 M d X i j )  = (mlk(z:j,, mzk(z:j), ’ ’ ’ , mnk(zf;)) for membership degree of pixel X i j  to class Ck. b i j (Ck)  is basic pixel information. Pixel Xi j  will verify the properties of class C k  with an intensity of b,j(Ck) = min f { m ~ ~ ( x ! ~ ) }  (9) where mr,(zij) is the membership function for each class (as defined above) Ck with respect to each f prop erty. 5 Comparison of each algorithm with the merging algorithm In order to compare the efficiency of the algorithm a bone marrow image will be processed. As has been previously mentioned, segmentation is defined as a de- composition of an image into homogeneous regions. In this context, the typical elements in a cell image are nucleus, cytoplasm, red blood cell, background, etc. The objective is to  present how the segmentation of the image can be improved by a fuzzy classification al- gorithm that considers the color characteristics of each pixel in the image. And also how the classification al- gorithm can be improved by a preprocessing labelled segmentation algorithm. However, the final objective for combining both algorithms is to be able to detect White Blood Cells (WBC) more precisely. WBC in bone marrow images show very changeable features due to the different maturity levels. The larger the cell the more immature it is. Cells become prw gressively smaller as they mature. The same way the nucleus of immature cells in the maturation sequence is relatively large in relation to the cytoplasm. As the cells age, the absolute and relative size of the nucleus decreases. In addition to this, cells frequently overlap each other. So, WBC can appear totally or partially hidden by other WBC or any other elements in the im- age. The goal here is to be able to automatically detect WBC, having a professional description of what can be identified in the image. Figure 1 represents the original image, where, be- sides white cells of different maturity levels, there are nucleated red blood cells, platelets and various types of grungy. Usually, immature white cells can be char- acterized by a round light nucleus. The rounder and lighter the nucleus is with respect to the cytoplasm, the less mature the cell is. Mature white cells can be characterized by a darker nucleus and a lighter cyto- plasm. Figure 2 represents the image processed by the segmentation algorithm [7]. Figure 3 represents the image processed by the fuzzy classification algorithm in 121. In the original image, figure 1, the nucleus can 488 Malure white cells Figure 1. Original Image be identified in all the cells as a blueish part. But there is not a remarkable difference between nucleated red blood cells and immature white cells. The mature white cells are identified by a lighter cytoplasm. The segmented image represents an improvement from the original image since the cytoplasm has been discrimi- nated from the nucleus, but white mature cells and red cells are still represented by the same colors. The nucleated red blood cells marked in the top part of the original image are classified as a whole nucleus cell, while the cell in the bottom part could be iden- tified as just cytoplasm since is been colored as the cytoplasm part in the white cells. Figure 4 represents the image that has been preprocessed by the perceptual segmentation algorithm [7] and the fuzzy classification algorithm [2]. This image represents an improvement over figures 2 and 3. For example, mature and imma- ture white cells have been classified in the same class and can be clearly distinguished from the immature cells represented in a lighter color. I t  can also be seen how the nucleolus can be distinguished in both mature and immature cells, being very noticeable the different rounded shape. As mentioned above, immature cells have a rounder and lighter nucleolus as can be clearly seen in figure 4. 6 Final comments The goal of this research was to combine two image processing algorithms based on different approaches to obtain a more precise classification of the im- Figure 2. Segmented Image Figure 3. Image Process by fuzzy clustering a I g o r i t h m 489 Figure 4. Segmented Image Process by fuzzy clustering algorithm age objects. The results demonstrate that both ap- proaches are enhanced by combining then in the pro- posed methodology. Also the advantage of using the fuzzy classification algorithm as a postprocessing has the advantage that we don’t need to define rules to identify the colors of the different elements. Therefore, the problems that arise because of the variation of col- ors in the different elements of the image due to the dyes, their quality, age, etc are avoided. Acknowledgements: This research has been par- tially supported by Smiths Aerospace as an IR&D project and by the Government of Spain, research grant BFM2002-0281. References J.C. Bezdek, and S. K. Pal Fuzzy Models for Pat- tern Recognition. New YorkIEEE Press 1992. A. Del Amo, J. Montero and G. Biging, “Classi- fying pixels by means of fuzzy relations,”Inter. J. General Systems, Vol. 29, pp. 605-621, 1999. A. Del Amo, J. Montero, A. Fernandez, M. Lopez, J. Tordesillas and G .  Biging, “Spectral fuzzy clas- sification: an application,” IEEE Tmns on Sys- tems, Man and Cybernetics, Part C, Vol. 32,pp. 42-48, Feb. 2002. A. Del Amo, J. Montero, and E. Molina, “Repre- sentation of consistent recursive rules”, European Journal of Operational Research, Vol. 130, pp. 2% 53, 2001. [5] A. Del Amo, D. Gbmez, J. Montero and G. Biging: ”Relevance and redundancy in fuzzy classification systems”. Mathware and Soft Computing 8203- 216, 2001. [6] A. Del Amo, J. Montero, G .  Biging and V. Cutello: ”Fuzzy classification systems”. European Journal of Operational Research, to appear. [i’] E. Montseny, P. Sobrevilla. ”Application of fuzzy techniques to the design of algorithms in computer vision”, Mathware d Soft Computing, Vol. 2-3, 1998, pp.223-230. [8] J. R. Jensen Introductory Digital Image Process- ing. A Remote Sensing Perspectiwe. Prentice Hall [9] P.P. Perny and B. Roy , “The use of fuzzy out- ranking relations in preference modelling”, Fuzzy Sets and Systems Vol. 49, pp. 33-53, 1992. [lo] Pratt W., Digital image processing, John Wiley and Sons, 1978. [ll] A.R. Robertson, ”Color perception”, Physics To- day, 1992, pp. 2429. [l2] S. Romani, E. Montseny, P. Sobrevilla. ”Obtaining the Relevant Colors of an image through Stability- based Fuzzy Color Histograms”, Proc. of the 12th IEEE International Conference on Fuzzy Systems, Sant Louis (MO), 2003, pp 914919. [13] A.R. Smith, ”Color gamut transform pairs”, IEEE Tmns on Computer Graphics, Vol. 2 ,  1978, pp. 12- 19. [14] J. Siskos, J. Lochard and J. Lombard , “A multi- criteria decision making methodology under fuzzi- ness: application to the evaluation of radiologi- cal protection in nuclear power plants, Fuzzy Sets and Decision Analysis in H.J.Zimmermann, L.A. Zadeh and B.R. Gaines (eds.) North Holland, Am- sterdam, 1984. [15] D. Yagi, K. Abe, H. Nakatani, ”Segmentation of color aerial photographs using HSV color models”, IAPR Workshop on Machine Vision Applications. (Tokyo), 1992. 490 

Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm

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Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm

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