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

(2017) Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm. Journal of medical signals and sensors. pp. 92-101. ISSN 2228-7477 (Print) 2228-7477 (Linking)

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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.

Item Type: Article
Keywords: K-means clustering segmentation thresholding watershed algorithm white blood cells
Divisions: Faculty of Medicine > Departments of Clinical Sciences > Department of Pathology
Page Range: pp. 92-101
Journal or Publication Title: Journal of medical signals and sensors
Journal Index: Pubmed
Volume: 7
Number: 2
ISSN: 2228-7477 (Print) 2228-7477 (Linking)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.mui.ac.ir/id/eprint/1530

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