title: Computer-assisted system with multiple feature fused support vector machine for sperm morphology diagnosis.
abstract: 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.
authors.affiliation: Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors.forename: Kuo-Kun
authors.initials: KK
authors.lastname: Tseng
authors.forename: Yifan
authors.initials: Y
authors.lastname: Li
authors.forename: Chih-Yu
authors.initials: CY
authors.lastname: Hsu
authors.forename: Huang-Nan
authors.initials: HN
authors.lastname: Huang
authors.forename: Ming
authors.initials: M
authors.lastname: Zhao
authors.forename: Mingyue
authors.initials: M
authors.lastname: Ding
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D008297:Male; D013094:Spermatozoa / Q000166:cytology*; D060388:Support Vector Machine