International Journal of Electrical and Computer Engineering (IJECE) Vol. 8, No. 3, June 2018, pp. 1731~1740 ISSN: 2088-8708, DOI: 10.11591/ijece.v8i3.pp1731-1740  1731 Journal homepage: http://iaescore.com/journals/index.php/IJECE Automatic Leukemia Cell Counting using Iterative Distance Transform for Convex Sets Nenden Siti Fatonah, Handayani Tjandrasa, Chastine Fatichah Departement of Informatic, Institute Teknologi Sepuluh Nopember, Indonesia Article Info ABSTRACT Article history: Received Jan 4, 2018 Revised May 2, 2018 Accepted May 11, 2018 The calculation of white blood cells on the acute leukemia microscopic images is one of the stages in the diagnosis of Leukemia disease. The main constraint on calculating the number of white blood cells is the precision in the area of overlapping white blood cells. The research on the calculation of the number of white blood cells overlapping generally based on geometry. However, there was still a calculation error due to over segment or under segment. This paper proposed an Iterative Distance Transform for Convex Sets (IDTCS) method to determine the markers and calculate the number of overlapping white blood cells. Determination of marker was performed on every cell both in single and overlapping white blood cell area. In this study, there were tree stages: segmentation of white blood cells, marker detection and white blood cell count, and contour estimation of every white blood cell. The used data testing was microscopic acute leukemia image data of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). Based on the test results, Iterative Distance Transform for Convex Sets IDTCS method performs better than Distance Transform (DT) and Ultimate Erosion for Convex Sets (UECS) method. Keyword: Acute leukemia Acute lymphoblastic leukemia Acute myloblastic leukemia Iterative distance transform for convex sets White blood cell Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Nenden Siti Fatonah, Departement of Informatic, Institute Teknologi Sepuluh Nopember, Indonesia. Email: nenden@mercubuana.ac.id 1. INTRODUCTION Leukemia is one of the most dangerous diseases and the impact is very deadly if not quickly overcome. Leukemia is caused by an abnormal development of white blood cells produced by bone marrow [1]. Research on acute leukemia cells has been done to support the medical diagnosis, whether it is about leukocyte segmentation or segmentation of nucleus cells. Manual calculations will certainly lead to less effective and weakness of accuracy due to the factor of subjectivity. A computerized system can be used as a support tool for physicians or pathology specialists in order to improve and accelerate the process of morphological analysis [2]. Saat ini beberapa penelitian menggunakan analisa dari data citra untuk mendiagnosa sebuah penyakit seperti pada literatur [3], [4]. One of the stages of the leukemia diagnosis system in the blood cell image data does the white blood cells segmentation. The process of segmentation is an important step because the results of good segmentation will get an accurate feature so as to improve the accuracy of the diagnosis. Currently, the process of segmentation of white blood cells has been done by many researchers [6]-[13]. In a research [6], segmentation process is done by thresholding using Zack Algorithm and eliminating background using arithmetic process. Segmentation based on the parametric model approach of a Gaussian mixture (GM) was used in the study [7]. In the study by Huang et al. [8], leukocyte segmentation by using Otsu thresholding method was previously done in the process of image repair. The segmentation of the nucleus using the Gram-