Colour Image Enhancement Techniques for Acute Leukaemia Blood Cell Mophological Features Aimi Salihah, A.N., M.Y.Mashor, Nor Hazlyna Harun Electronic & Biomedical Intelligent Systems (EBItS) Research Group, School of Mechatronics Engineering, University Malaysia Perlis, 02600 Ulu Pauh, Perlis, Malaysia. E-mail: aimi_salihah@yahoo.com Abstrat-Image enhancement plays an important role in computer vision and image processing. In this paper, image enhancement was used to eliminate the background noise and improve the image quality for the purpose of determining the focal areas such as nucleus, Auer rode and nucleoli in acute leukaemia images. In this paper, the use of three contrast enhancement techniques for colour images using RGB components is proposed. The three contrast enhancement techniques are partial contrast, bright stretching and dark stretching. The contrast enhancement techniques enhance the morphological features of acute leukaemia images to ease the leukaemia classiication between Acute Lymphoblastic Leukaemia (ALL) and Acute Myelogenous Leukaemia (AML). The results show that partial contrast is the best technique that helps to improve the image visibility while preserving the signiicant features of acute leukaemia images. Hence, the resultant images would become useful to Hematologists for further analysis of acute leukaemia. KWors-Leukaemia, image processing, contrast enhancement, partial contrast, bright and dark stretching. I. INTRODUCTION The tem leukaemia refers to a group of cancers of the blood cells. It is characterized by abundance of abnormal white blood cells (blast) in he body. Acute leukaemia is a rapidly progressing disease compare to the chronic leukaemia. It primarily afects cells that are not ully developed or diferentiated. The two main types of acute leukaemia are Acute Lymphoblastic Leukaemia (ALL) and Acute Myelogenous Leukaemia (AML) [1]. Generally in leukaemia diagnosis, Hematologists will look for the abnormal white blood cells to diferentiate the types of leukaemia either ALL or AML. There are several mophological features that can be used to distinguish between ALL and AML such as size and shape of blast [2]. In Malaysia, leukaemia was reported to be the 5 h out of 10 most requent cancers in male and female between 2003 until 2005. In addition, leukaemia causes more death than any other cancers among chilren and young adults under the age of 20 [3]. Based on these, the requirement for fast analysis of blood cells for leukaemia is of paramount importance in the 978-1-4244-6588-0/10/$25.00 ©2010 IEEE H.Rosline Department of Haematology, School of Medical Sciences, University Science Malaysia, Kubang Kerian, Kelantan, Malaysia. E-mail: roslin@kb.usm.my healthcare industry. Currently, the microscopic investigation to identiy the types and maturity of blood cells is perfomed manually by Hematologists through visual identiication under the microscope. Speciic type of leukaemia must be classiied in order to provide the best treatment. However, the manual recognition method requires a lot of time and efort [4]. This method is therefore inappropriate to be utilized in large hospitals. Several algorithms and techniques have been developed for blood cells recognition [5]. Image enhancement at the pre processing stage becomes the most importnt process for a successul feature extraction and diagnosis of leukaemia [6]. In general, there are two requirements to be ulilled for colour image enhancement [6]. The irst one is to keep the colour structure of the original image. This can be done by simply keeping the ratios between R, G, and B components of every pixel. The second requirement is to present as much information as the original. This can be achieved by using the information in the luminance component as well as colour components [6]. Most works proposed he used of gray level image processing techniques to extract the blood cell features [7][8]. However, the actual screening process by Hematologists is performed on stained slide where the leukaemia is detected based on colour and size of blast [2]. Kumar, Verma, and Singh [8] stated that colour images are very rich source of information, because they provide better description of a scene. The conversion of the colour image to gray level image may cause some features that are based on colour to disappear. To overcome the problem, identiication of blood cells based on colour and shape were proposed [9]. However, the availability of the colour image processing algorithms is quite limited [4][5]. Contrast enhancement technique is widely used to increase the visual image quality [10][11]. Exposure of the microscope also inluences the quality of captured images [12]. Overexposure setting will lead to producing briht image, while underexposure setting will produce a drk 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 3677