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BioMed Research International
Volume 2013, Article ID 687607, 13 pages
Research Article

Computer-Assisted System with Multiple Feature Fused Support Vector Machine for Sperm Morphology Diagnosis

1Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
2Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
3Department of Mathematics, Tunghai University, Taichung 40704, Taiwan
4Huazhong University of Science and Technology, Wuhan 430074, China

Received 15 April 2013; Revised 4 July 2013; Accepted 23 July 2013

Academic Editor: Lei Chen

Copyright © 2013 Kuo-Kun Tseng et al. 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.


Sperm morphology is an important technique in identifying the health of sperms. In this paper we present a new system and novel approaches to classify different kinds of sperm images in order to assess their health. Our approach mainly relies on a one-dimensional feature which is extracted from the sperm’s contour with gray level information. Our approach can handle rotation and scaling of the image. Moreover, it is fused with SVM classification to improve its accuracy. In our evaluation, our method has better performance than the existing approaches to sperm classification.