title: Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm.
abstract: Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.
authors.affiliation: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.
authors.forename: Yazan M
authors.initials: YM
authors.lastname: Alomari
authors.affiliation: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.
authors.forename: Siti Norul Huda
authors.identifier: 0000-0002-2602-7805
authors.initials: SN
authors.lastname: Sheikh Abdullah
authors.affiliation: Department of Pathology, UKM Medical Center, Universiti Kebangsaan Malaysia, Cheras, 56000 Kuala Lumpur, Malaysia.
authors.forename: Raja
authors.initials: R
authors.lastname: Zaharatul Azma
authors.affiliation: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.
authors.forename: Khairuddin
authors.identifier: 0000-0003-1794-019X
authors.initials: K
authors.lastname: Omar
mesh_terms: D000465:Algorithms; D001772:Blood Cell Count; D003198:Computer Simulation; D004912:Erythrocytes / Q000166:cytology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007962:Leukocytes / Q000166:cytology*; D008853:Microscopy / Q000379:methods; D012044:Regression Analysis; D015203:Reproducibility of Results; D012984:Software