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Biomedical Electronic Engineering Program, School of Mechatronic Engineering, Universiti Malaysia Perlis, Kangar, Perlis 2 Mechatronic Engineering Program, School of Mechatronic Engineering, Universiti Malaysia Perlis, Kangar, Perlis 3 Hematology Department, University Hospital, University Science Malaysia, Kubang Kerian, Kelantan 4 Patalogy Department, Hospital Tuanku Fauziah, Perlis

Abstract — Image processing technique involved five basic components which are image acquisition, image preprocessing, image segmentation, image post-processing and image analysis. The most critical step in image processing is the segmentation of the image. In this paper, we review some of the general segmentation methods that have found application in classification in biomedical-image processing especially in blood cell image processing. Basically, segmentation of the image divides the whole image into some unique disjoint regions. The fact that the segmented image should retain maximum useful information and discard unwanted information makes the whole process critical. Keywords — Image segmentation, Image processing, Blood segmentation, Blood cells and White Blood Cell.

I. INTRODUCTION Over the last 15 years, several research groups have focused on the development of computerized systems that can analyze different types of medical images and extract useful information for the medical professional [1]. Most of the proposed methods use images acquired during a diagnostic procedure. The major objectives of image analysis in biomedical instrumentation engineering are to gather the information, screening or investigating, to diagnose, therapy and control, monitoring and evaluation. It is important always to bear in mind that the main purpose of biomedical imaging and image analysis is to provide a certain benefit to the subject or patient [2]. Cell classification has widespread interest especially for clinics and laboratories. For example, patient’s blood cells counting is use to extract information about other cells that are not normally present in peripheral blood but may be released in certain disease processes by the hematologist [3]. One of the great challenge to engineer especially biomedical engineer is to transform this human practical task into computer based which the system is comparable to human performance or better. Thus, the system must be stable and able to handle the uncertainty. Up to now, automatic cell classification systems can not meet the complexity of real clinical demands [4][5].

II. BLOOD

CELLS MORPHOLOGY

Among all of the body’s tissues, blood is unique due to its existence as the only fluid tissue. A blood cell can be any type of cell normally found in blood which falls into four categories which are red blood cell (RBC), white blood cell (WBC), platelet and plasma [6]. The differences between these groups lie on the texture, color, size and morphology of nucleus and cytoplasm. In blood smear, number of red cells is many more than white blood cells. For example an image may contain up to 100 red cells an