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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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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.

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Acknowledgments

The authors would like to express their sincere thanks and appreciation to Dr. Jaswant Singh, Western College of Veterinary Medicine, University of Saskatchewan, for permitting the use of his images in this study, and for valuable advice rendered.

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Authors and Affiliations

  1. Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada

    Meng Dong & Mark G. Eramian

  2. Department of Computer Science, North Dakota State University, Fargo, ND, USA

    Simone A. Ludwig

  3. Department of Obstetrics, Gynecology and Reproductive Sciences, University of Saskatchewan, Saskatoon, SK, Canada

    Roger A. Pierson

Authors
  1. Meng Dong
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  2. Mark G. Eramian
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  3. Simone A. Ludwig
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  4. Roger A. Pierson
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Corresponding author

Correspondence to Mark G. Eramian.

Additional information

This research was supported, in part, by the Natural Sciences and Engineering Research Council of Canada, Grant Number RGPIN262027-03.

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Dong, M., Eramian, M.G., Ludwig, S.A. et al. Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns. Med Biol Eng Comput 51, 405–416 (2013). https://doi.org/10.1007/s11517-012-1009-2

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  • DOI: https://doi.org/10.1007/s11517-012-1009-2

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