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

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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