Computer-assisted system with multiple feature fused support vector machine for sperm morphology diagnosis.
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 onedimensional feature which is extracted from the s...
| Publicado en: | BioMed Research International Vol. 2013; pp. 687607 - 687608 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104111271&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104111271 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104111271 2012358768 NLM24191249 PMC3803132 104111271 ppf: 687607 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Computer-assisted system with multiple feature fused support vector machine for sperm morphology diagnosis. aug: au: Tseng, Kuo-Kun Li, Yifan Hsu, Chih-Yu Huang, Huang-Nan Zhao, Ming Ding, Mingyue affil: Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong 518055, China. sug: subj: Image Processing, Computer Assisted Equipment and Supplies Image Processing, Computer Assisted Methods Spermatozoa Algorithms Male Male ab: 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 onedimensional 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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