Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns

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@Article{journals/mbec/DongELP13,

• author = "Meng Dong and Mark G. Eramian and Simone A. Ludwig and Roger A. Pierson",
• title = "Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns",
• journal = "Medical and Biological Engineering and Computing",
• year = "2013",
• number = "4",
• volume = "51",
• keywords = "genetic algorithms, genetic programming",
• bibdate = "2013-03-06",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/mbec/mbec51.html#DongELP13",
• pages = "405--416",
• URL = "http://dx.doi.org/10.1007/s11517-012-1009-2",
• DOI = "doi:10.1007/s11517-012-1009-2",
• 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 percent of the images. The segmentation algorithm achieved a mean (pm standard deviation) sensitivity and specificity of 0.8693 pm 0.1371 and 0.9136 pm 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 pm 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 pm 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable.",

}

Genetic Programming entries for Meng Dong Mark G Eramian Simone A Ludwig Roger A Pierson

Citations