International Journal of Computer Applications (0975 – 8887) Volume 90 – No 7, March 2014 48 Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis based on HSV Color Space Kamal A. ElDahshan Professor of Computer Science, Dept. of Mathematics, Faculty of Science, AL-AZHAR University, Cairo, Egypt Mohammed I. Youssef Dept. of Electronic Engineering, Faculty of Engineering, AL-AZHAR University, Cairo, Egypt Emad H. Masameer, Ph.D Assistant Professor of Computer Science, Dept. of Mathematics, Faculty of Science, AL-AZHAR University, Cairo, Egypt Mohammed A. Mustafa Lecturer assistant, Dept. of Management Information Systems, Modern Academy for Computer Science and Information Technology, Cairo, Egypt ABSTRACT Image segmentation is considered the most critical step in image processing and plays a vital role in computer vision especially in the medical field. In this work, the segmentation framework based on the color perception characteristics of eyes for acute lymphoblastic leukemia (ALL) images is proposed to segment each leukemia image into two regions: blasts and background. This work is based on nonlinear transformation of microscope color images from RGB color space to HSV color space. In the HSV color space, hue channel is used as a method in segmentation of WBC from its complicated background. The results show that the proposed segmentation framework can differentiate well between normal bone marrow and ALL and become useful for hematologists in further analysis. General Terms Medical Image Processing, Image Segmentation, Color Spaces Keywords Microscope Images, Segmentation, ALL, HSV 1. INTRODUCTION Leukemia is a type of cancer caused by abnormal increase of the white blood cells. Yearly thousands of people die of leukemia throughout the world due to the nature of Leukemia cells that become out of control and they spread independently. Early diagnosis and treatment that are applied to the correct cells are vital. Leukemia can be classified into acute and chronic. Acute leukemia spreads very rapidly and has to be treated promptly rather than chronic leukemia that does not have to be prompt. Acute leukemia can be either lymphoblastic (ALL) or myelogenous (AML), based on affected cell type. Chronic leukemia can be either lymphoblastic (CLL) or myelogenous (CML) [1]. Acute lymphoblastic leukemia (ALL) is considered the prime focus of this work, which has a higher expectation of survival rate compared to AML. Segmentation is one of the most demanding tasks in image processing. It is used in Computer Vision to automatically partitioning a digital image into a number of different meaningful regions. For biomedical imaging applications, image segmentation is a founding step in image analysis as it will directly affect the post-processing. It is a crucial component in diagnosis [2] and treatment [3]. The main goal of acute leukemia blood cell segmentation is to extract component such as blast from its complicated blood cells background. There are many techniques that have been developed for image segmentation such as threshold techniques [4], edge detection [5] and watershed techniques [6]. Due to the complex nature of blood cells and overlapping between these cells, segmenting them remains a challenging task [7]. Many algorithms for segmentation have been developed for grayscale images rather than color images which require more information to be processed [8]. In order to simplify enhancement and segmentation for leukemia color images, transformations of original RGB images to different color spaces such as (HSI, HSL, YUV, XYZ, Lab…etc.) are proposed in many works. This transformation reduces correlation between different color channels compared to RGB color space and enables dealing with these channels separately which reduces the complexity of the enhancement and segmentation problems and saves computation time. Based on HIS color space, enhancement technique [9] and Segmentation [10] for leukemia images is proposed. According to [11], Lab color space is used for segmentation process. This work focuses on HSV color space for segmentation as it provides useful chromatic information of the leukemia images [12]. 2. FRAMEWORK 2.1 Images Dataset Microscope Images of ALL are taken from ALL-IDB database [13]. The images of the database have been captured with an optical laboratory microscope coupled with a Canon Power Shot G5 camera. All images are in JPG format with 24 bit color depth, resolution 2592 × 1944. The images are taken with different magnifications of the microscope ranging from 300 to 500. ALL-IDB2 version of the database is used. Figure 1 shows the sample of ALL images.