Nucleus Segmentation Technique for Acute Leukemia

N.H.Abd Halim*1, M.Y.Mashor*2, A.S.Abdul Nasir*3,N.R.Mokhtar*4, H.Rosline#5 *Electronic & Biomedical Intelligent Systems (EBItS) Research Group,

School of Mechatronics Engineering, University Malaysia Perlis, 02600 Ulu Pauh, Perlis, Malaysia.

1E-mail: nurul.hazwani43@yahoo.com #Department of Hematology, School of Medical Science, University Science Malaysia,

Kubang Kerian, Kelantan, Malaysia. 5E-mail: roslin@kb.usm.my

Abstract—Leukemia is a disease that affects blood forming cells in the body. Early detection of the disease is necessary for proper treatment management. Abnormal white blood cells or blasts play important role for hematologists in their diagnostic process. Digital image processing technique could help them in their analysis and diagnosis by enhancing the visibility of the interested features of the WBC. In this paper, a global contrast stretching (GCS) and segmentation based on HSI (Hue, Saturation, Intensity) color space will be used to improve the image quality. Image enhancement is very important to increase the visual aspect of blast cells. The results show that the proposed image enhancement procedure is useful to extract the nucleus region in WBC images sample by using the same threshold value, for both ALL and AML images.

Keywords— Acute Leukemia, Contrast Enhancement, Global Contrast Stretching, Nucleus Segmentation, HSI Color Space.

I. INTRODUCTION Leukemia is a group of blood diseases affecting the blood-

cells and most commonly white blood cells or leukocytes. Leukemia is characterized by overproduction of abnormal (or immature) white cells which are unable to fight infection. There are two main types of acute leukemia: Acute Lymphoblastic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). Acute leukemia is a rapidly progressing disease that affects mostly cells that are unformed (not yet fully developed or differentiated) [1]. ALL is the most common type of leukemia in young children. This disease also affects adults, especially those aged 65 and older [2]. While, AML is commonly occurs in adults than in children, and more commonly in men than women [3].

The early and fast identification of the leukemia type, greatly aids in providing the appropriate treatment for the particular type. Its detection starts with a complete blood count (CBC). If there are abnormalities in this count, patient should perform bone marrow biopsy .Therefore, a study of morphological bone marrow and peripheral blood slide analysis is done to confirm the presence of leukemic cells. In order to classify the abnormal cells in their particular types and subtype of leukemia, an expert operator will observed some cells under a light microscopy looking for the

abnormalities presented in the nucleus or cytoplasm of the cells. This classification is very important to determine which treatment should be given to the patient [4]. However, this analysis suffers from slowness and it presents a not standardized accuracy since it depends on the operator’s capabilities and tiredness. Therefore, this paper presents a fast and effective segmentation procedure for blast images which is very helpful for improving the hematological procedure and accelerating diagnosis of leukemia diseases.

Image enhancement at the preprocessing stage becomes the most crucial process for a successful feature extraction and diagnosis of leukemia. Several previous studies had proved that contrast enhancement technique is capable to improve the medical image quality [5].Hassan and Aakamatsu [6] discussed about methods for image enhancement of dark blurred image by using robust techniques. So as to enhance the contrast of the segmented Pap smear image for easier interpretation of abnormal cervical cells; Mat Isa, Mashor, & Othman [7] used linear contrast enhancement algorithm.

Due to the accuracy of the subsequent feature extraction and classification depends on the correct segmentation of blasts, hence the segmentation step plays important role. As for the blood cell images, due to the cells complex nature it still remains a challenging task to segment them. Moreover, many algorithms for segmentation have been developed for grayscale images [8]. The purpose of segmentation is to change the representation of an image to something that is more meaningful and easier to analyze [9]. A novel cell detection method that utilizes both intensity and shape information of cell to improve the segmentation was proposed by Wang et al. [10]. Kumar et al. [11] discussed about an approach for color image segmentation. Kumar stated that color images provide a better description of a scene as compared to gray scale images as it is very rich source of information. In fact, the actual screening process by hematologists is carried out on stained slide where the leukemia is detected based on color and size of blast [12].Thus, color segmentation becomes a very important issue.

By using the combination of partial contrast and bright stretching techniques, the fully segmented blast which consist of cytoplasm and nucleus region can be achieved [13].For

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nucleus segmentation; these can be achieved by using the combination of partial contrast and dark stretching techniques. The current study investigates the performance of global contrast enhancement techniques that have been applied to enhance area of interest in blood images and color image segmentation using HSI color space in order to obtain the fully segmented nucleus of Acute Lymphoblastic Leukemia and Acute Myelogenous Leukemia.

II. METHODOLOGY Most of the time, hematologist are interested on white

blood cells (WBC) only. The visual aspect of white blood cells and red blood cells can be distinguished based on color where WBC inclines to appear in blue or purple. In order to classify whether the leukemia is ALL or AML, the morphological features will be discovered based on these characteristics [14]: a) The size and shape of white blood cells nucleus. For

example, ALL rather small, uniform blast cells with scanty cytoplasm and rounded with usually a single nucleolus. In contrast, AML have blasts with large, often irregular and nuclei with one or more nucleoli.

b) The presence of Auer rode and multiple nucleoli inside the nucleus, which are prominent in AML.

In this research, the main objective of acute leukemia blood cells image enhancement is to enhance the contrast of features of interest such as nucleus, cytoplasm, Auer rode and nucleoli from the complicated blood cells background by using contrast enhancement techniques. Then, several image segmentation techniques will be developing to segment the WBC from the background.

A. Contrast Enhancement for Acute Leukemia Using Global Contrast Technique The quality of the image depends on the exposure of the

microscope and staining process. Overexposure setting will contribute to producing bright image; on the other hand underexposure setting will produce a dark image. Due to the low quality of the image, it will be hard to visualize and analyze the blood cell morphological features on the screen for further image processing.

Image enhancement processes consist of a collection of techniques that attempt to convert the image to a form better suited for analysis by a human or machine or to improve the visual appearance of an image [12]. Up to now, contrast stretching process plays an important role in enhancing the quality and contrast of medical images.

The procedure used to develop the contrast enhancement techniques are as follows: Step 1 : Capture the acute leukemia slide image at 40X

magnification and save as bitmap (*.bmp) Step 2 : Select the original image of ALL and AML. Step 3 : Plot the histogram to determine the threshold value. Step 4 : Apply the global contrast enhancement techniques

by implementing the threshold and stretching value on the selected images.

Global contrast stretching (GCS) is performed by applying sliding windows (called the KERNEL) across the image and adjusting the center element using the formula [15]:

Ip(x, y) = 255•[Iο(x, y) −min]/(max−min) (1) where, Ip(x, y) is color level output for the pixel(x,y) from the contrast stretching process. Iο(x, y) is the color level data input for the pixel(x,y). max - is the maximum value for color level in the input image. min - is the minimum value for color level in the input image. From the formula, (x, y) are the coordinates of the center picture element in the KERNEL; min and max are the minimum and maximum values of the image data in the selected KERNEL [16].

Global contrast stretching (GCS) will consider all range of color palates at once to determine the maximum and minimum for all RGB color image. The combination of RGB color will give only one value for maximum and minimum for RGB color. This maximum and minimum value will be used for contrast stretching process.

B. Nucleus Segmentation Using S-components The representation of color images by Red-Green-Blue color model is the conventional method. But the three components of RGB color model are highly correlated, so that the chromatic information is not suitable for direct processing. Due to color space is convenient to convert from RGB and also intimately related to human perception, therefore, the HSI (Hue, Saturation and Intensity) color space is adopted [17]. Thus, out of these three color spaces, only the S component is used for transforming the RGB image by using the following equation [4]:

Saturation = 1 - _____ 3______ min(R, G, B) (2) R+G+B

The procedures used to develop the image processing for blast segmentation are as follows: Step 1 : Extract the S-component information from the

enhanced RGB image. Step 2 : In order to obtain threshold value, the S component

histogram (S-plot) will be developed based on S component image.

Step 3 : Apply the thresholding technique by implementing the same threshold value (TH=100) on S component image of ALL and AML. Only threshold value at 100 is suitable to fully segment of nucleus for both images.

Step 4 : Utilize N x N (N=7) median filter to the resulted image in order to smooth the image and maintain

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the edges. Step 5 : Region growing technique is used in order to obtain

the region area in pixels for blast and nucleus. Step 6 : The color for the segmented blast is retrieved

from the global contrast image.

III. RESULTS & DISCUSSION Figure 1 and 2 represent the original images of Acute

Lymphoblastic Leukemia (ALL) and Acute Myelogenous Leukemia (AML), respectively. In order to recognize the significance of image enhancement technique on enhancing the morphological features of blood cells, comparison between the original images and after enhancement is needed. Figure 3 and 4 represent the global contrast stretching (GCS) images for both ALL and AML, respectively. Based on the resultant ALL and AML images, global contrast produces brighter images and the characteristic cells such as nucleus and cytoplasm can be seen easily compare to the original image. Here, the histogram can be used in order to identify the significance of enhancement technique on improving the contrast of acute leukemia images. Based on the resultant of intensity histogram in Figure 3 and 4, the narrow range of data in original image will be stretched linearly to a wider part of the intensity histogram so that its dynamic range is fulfilled. After the enhanced images had been obtain, the images segmented in order to obtain the fully segmented nucleus. Here, the images had been segmented by using the color image segmentation based on HSI color space. Figure 5 and 6 represent the S component images for both ALL and AML images, respectively. Since the contrast of the nucleus have been increased due to the application of contrast enhancement technique, it will be easy to suppress the background from the image. The threshold value was selected at 100 (TH=100) and used to segment the background from the image. The result of fully segmented nucleus obtained in Figure 7 and 8 show the resultant segmented nucleus for both ALL and AML images, respectively. Even though the threshold value used is fixed to 100, the value is found to be suitable for both ALL and AML images. After the segmented nucleus has been obtained, the difference of characteristic of morphological features can be seen from both ALL and AML images in terms of size and shape based features. ALL is rather small size in term of blasts compare to AML. Hence, the proposed segmentation method using combination of GCS and Saturation component is found suitable to be applied on most ALL and AML images.

IV. CONCLUSION The results show that the proposed global contrast

enhancement technique and segmentation based on HSI color space produced good segmentation performance on Acute Lymphoblastic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). In addition, the fully segmented nucleus can be achieved by using the same threshold value even though different samples of ALL and AML had been used. Thus, the resultant images can assist the hematologist in classifying blood sample as either ALL or AML. For future

work, the result of this paper can be used as the basis for extracting the features from the blood slide images.

ACKNOWLEDGMENT The author is gratefully acknowledged and thanks the team members of leukemia research and University Science of Malaysia (USM). The author also likes to acknowledge Malaysia Government for awarding their National Science Fund (NSF) scholarship and support throughout this project.

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[3] G.A.Colvin, G.J.Elfenbein.”The latest treatment advances for acute myelogenous leukemia,” Med Health R I, Vol.86, No.8,2003, pp 243-246

[4] C.Reta, L.Alamirano, J.A.Gonzales, R. Diaz,and J.S.Guichard, “ Segmentation of Bone Marrow for Morphological Classification of Acute Leukemia,” in Proceding of the Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS 2010).

[5] N. Mustafa, N. A. Mat Isa, M. Y. Mashor, and N. H. Othman, “ColourContrast Enhancement on Preselected Cervical Cell for ThinPrep® Images,” in Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing(IIH-MSP 2007), Kaoshing, Taiwan, 2007.

[6] N.Y.Hassan and N.Aakamatsu, “ Contrast Enhancement Technique of Dark Blurred Image,” in International Journal of Computer Science and Network Security, vol.6 no2A, February 2006.

[7] N.A.Mat Isa, M.Y. Mashor, & N.H. Othman, “Contrast Enhancement Image Processing on Segmented Pap Smear Cytology Images,” in Int. Conf. on Robotic, Vision, Information and Signal Processing.pp 118 – 125, 2003.

[8] W. Skarbek and A. Koschan :Colour Image Segmentation- A Survey, Technical Report 94-32,Technical University of Berlin, Department of Computer Science, October 1994.

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[11] R.S.Kumar, A.Verma and J. Singh: “ Color Image Segmentation and Multi-Level Thresholding by Maximization of Conditional Entrophy”, in International Journal of Signal Processing Vol 3, No. 1, 2006, WASET. ORG

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[14] S. Miwa, Atlas of Blood Cells. Tokyo, Japan: Bunkodo Co., Ltd., 1998. [15] A.D Lascari.: General aspects. Leukemia in childhood, fifth Edition,

Springfield, Illinois, Charles C Thomas,1973; pp 3-29. [16] I.Attas,J.Belward and J.Loise, “A variational approach to the radiometric

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[17] B.V.Chandra, R.Hegadi and M. Malemath, “Analysis of Abnormality in Endoscopic images using Combined HSI Color Space and Watershed

Segmentation” in 18 th IEEE International Conference on Pattern Recognition(ICPR’06).

(a) Image ALL 1 (b) Image ALL 2 (c) Image ALL 3

Figure 1: Original image for Acute Lymphoblastic Leukemia (ALL).

(a) Image AML 1 (b) Image AML 2 (c) Image AML 3

Figure 2: Original image for Acute Myelogenous Leukemia (AML).

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(a) Image ALL 1 (b) Image ALL 2 (c) Image ALL 3

Figure 3: Global Contrast Stretching for Acute Lymphoblastic Leukemia (ALL).

(a) Image AML 1 (b) Image AML 2 (c) Image AML 3

Figure 4: Global Contrast Stretching for Acute Myelogenous Leukemia (AML).

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(a) Image ALL 1 (b) Image ALL 2 (c) Image ALL 3

Figure 5: S-component image for Acute Lymphoblastic Leukemia (ALL).

(a) Image AML 1 (b) Image AML 2 (c) Image AML 3

Figure 6: S-component image for Acute Myelogenous Leukemia (AML).

(a) Image ALL 1 (b) Image ALL 2 (c) Image ALL 3

Figure 7: Segmented image based on S component (TH=100) for Acute Lymphoblastic Leukemia (ALL).

(a) Image AML 1 (b) Image AML 2 (c) Image AML 3

Figure 8: Segmented image based on S component (TH=100) for Acute Myelogenous Leukemia (AML).

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