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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 7235795, 9 pages
https://doi.org/10.1155/2018/7235795
Research Article

Simultaneous Segmentation of Leukocyte and Erythrocyte in Microscopic Images Using a Marker-Controlled Watershed Algorithm

1College of Electrical and Information Engineering, Hunan University, Changsha, China
2Kunshan Hunan University Robot Technology Co., Ltd., Kunshan, China

Correspondence should be addressed to Changyan Xiao; nc.ude.unh@oaix.c

Received 7 September 2017; Revised 15 December 2017; Accepted 18 January 2018; Published 22 February 2018

Academic Editor: Michele Migliore

Copyright © 2018 Huisi Miao and Changyan Xiao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The density or quantity of leukocytes and erythrocytes in a unit volume of blood, which can be automatically measured through a computer-based microscopic image analysis system, is frequently considered an indicator of diseases. The segmentation of blood cells, as a basis of quantitative statistics, plays an important role in the system. However, many conventional methods must firstly distinguish blood cells into two types (i.e., leukocyte and erythrocyte) and segment them in independent procedures. In this paper, we present a marker-controlled watershed algorithm for simultaneously extracting the two types of blood cells to simplify operations and reduce computing time. The method consists of two steps, that is, cell nucleus segmentation and blood cell segmentation. An image enhancement technique is used to obtain the leukocyte marker. Two marker-controlled watershed algorithms are based on distance transformation and edge gradient information to acquire blood cell contour. The segmented leukocytes and erythrocytes are obtained simultaneously by classification. Experimental results demonstrate that the proposed method is fast, robust, and efficient.