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Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm Publisher



Ghane N1 ; Vard A2 ; Talebi A3 ; Nematollahy P3
Authors
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Authors Affiliations
  1. 1. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Student Research Center, Isfahan, Iran
  2. 2. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Medical Image, Signal Processing Research Center, Isfahan, 81745, Iran
  3. 3. Department of Pathology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Medical Signals and Sensors Published:2017


Abstract

Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologists in the diagnostic process can be so effective. Segmentation of WBCs is usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, k-means clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell's image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method. © 2017 Journal of Medical Signals & Sensors.
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