title: Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
authors.forename: Meng
authors.initials: M
authors.lastname: Dong
authors.forename: Mark G
authors.initials: MG
authors.lastname: Eramian
authors.forename: Simone A
authors.initials: SA
authors.lastname: Ludwig
authors.forename: Roger A
authors.initials: RA
authors.lastname: Pierson
mesh_terms: D000465:Algorithms; D000818:Animals; D002417:Cattle; D003338:Corpus Luteum / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D005260:Female; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010053:Ovary / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D014463:Ultrasonography