978-1-5386-7942-5/19/$31.00 ©2019 IEEE Automated Cell Counting System For Chronic Leukemia Nor Hazlyna Harun School of Computing Universiti Utara Malaysia Sintok, Kedah, Malaysia Country hazlyna@uum.edu.my Nur Azzah Abu Bakar School of Computing Universiti Utara Malaysia Sintok, Kedah, Malaysia nurazzah@uum.edu.my Uvaghesvary A/P Mohan School of Computing Universiti Utara Malaysia Sintok, Kedah, Malaysia Country uvagesmohan95@gmail.com Maslinda Mohd Nadzir School of Computing Universiti Utara Malaysia Sintok, Kedah, Malaysia maslinda@uum.edu.my Mohamad Ghozali Hassan School of Technology Management and Logistic Universiti Utara Malaysia Sintok, Kedah, Malaysia ghozali@uum.edu.my Robiyanti Adollah Mechatronic School of Engineering Universiti Malaysia Perlis Arau, Perlis, Malaysia robiyanti@unimap.edu.my Abstract— Leukemia is a group of cancers which create a large amount of immature white blood cells. Abnormal numbers of white blood cells may suggest a screening of leukemia, and the blood sample is examined under the microscope to observe if the cells appear abnormal. The manual screening of chronic leukemia is time consuming and tedious while the Automated Hematology Analyzer is too expensive, particularly for the third world countries. This has been made exacerbated by the gold standard of biopsy inspiration which is painful and invasive for the patient. An automated cell counting (ACC) system for chronic leukemia has been developed to support and ease the routine of hematologist and technologist in the screening process and to give a quick and accurate result. The fusion of image processing technique has been proposed, which include four main stages, i.e. image acquisition, image segmentation, noise removal and counting process. Based on the sensitivity test over 100 images of chronic cells, an overall result shows 98.94% sensitivity of the system performance and the processing time recorded is less than 6 second per image. This proved an excellent level of ACC system performance. It is concluded that the system is suitable to be used as an automated counting system for chronic leukemia disease due to its sensitivity and ability to reduce the time taken for screening process. Keywords—Automated Cell Counting System (ACC), Segmentation, Morphological Operation, Blood Cell. I. INTRODUCTION Leukemia is a group of cancers which create a large amount of immature blood cells. These immature blood cells take up space in the bone marrow, preventing the bone marrow from making healthy blood cells such as platelets, red blood cells, and white blood cells. Leukemia can be classified into two, which are chronic leukemia and acute leukemia [1]. Acute leukemia grows rapidly and will spread over the body within a short period. Whereas, chronic leukemia grows slower than acute leukemia and become more serious over years [2]. According to [3], screening process based on microscopic blood images appeared to have an error rate between 30 to 40% depending on the haematologists’ experience and also the difficulties to distinguish between the normal and abnormal cells [4]. The utilization of image processing technique as an automated counting system for diagnosis of leukemia disease has been studied in [5] and [6] in which the image segmentation technique such as K-mean clustering is employed in several leukemia cases. In [7], K-mean clustering is used for automated acute lymphocytic leukemia detection. While in [8], K-mean clustering is integrated with histogram equalization and Zack algorithm to determine leukemia. Recent study also used K-mean clustering to identify the leukemia cells and features extraction, and image renement [9]. Another study presented the integration of K- mean clustering and machine learning technique to differentiate among Acute lymphoblastic leukemia (ALL), Acute myeloid leukemia (AML), Chronic lymphocytic leukemia (CLL) and Chronic myeloid leukemia (CML) [10]. What is learnt from the above is the effectiveness of image processing technique in automating leukemia diagnosis. However, most of the studies in the field of image processing only focused on cases outside Malaysia. Hence, this study serves as a replication study which focuses on leukemia cases in Malaysia. The key contribution of this study is the development of an ACC for Chronic Leukemia to improve the reliability of the counting blood cell and decrease the dependency on human experts. ACC is introduced to automatically count chronic leukemia cell and at the same time promised an acceptable result for screening process based on blood sample. Details of the development of the system is discussed in Section II and III. Section IV of this paper concludes the work that is presented in the earlier sections. II. DEVELOPMENT OF ACC SYSTEM ACC was developed using MATLAB environment which utilized image processing technique for chronic cell counting based on the blood samples. There are four stages including image acquisition, image segmentation, noise removal and counting process. Fig. 1 indicates the combination of image  502 brought to you by CORE View metadata, citation and similar papers at core.ac.uk provided by UUM Repository