Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns.
In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The de...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 4; pp. 405 - 417 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2013
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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=104247028&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104247028 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2013 vid: 51 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104247028 NLM23229646 2012034941 10.1007/s11517-012-1009-2 NLM23229646 104247028 ppf: 405 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns. aug: au: Dong, Meng Eramian, Mark G Ludwig, Simone A Pierson, Roger A affil: Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada. sug: subj: Image Processing, Computer Assisted Methods Information Science Methods Ovary Ultrasonography Algorithms Animal Studies Cattle Female Ovary Anatomy and Histology Reproducibility of Results Software Female ab: In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 % of the images. The segmentation algorithm achieved a mean (±standard deviation) sensitivity and specificity of 0.8693 ± 0.1371 and 0.9136 ± 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the segmented boundary from the true boundary was 1.12 ± 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 ± 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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