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Segmentation of the Proximal Femur by the Analysis of X-ray Imaging Using Statistical Models of Shape and Appearance

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10842))

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Abstract

Using image processing to assist in the diagnostic of diseases is a growing challenge. Segmentation is one of the relevant stages in image processing. We present a strategy of complete segmentation of the proximal femur (right and left) in anterior-posterior pelvic radiographs using statistical models of shape and appearance for assistance in the diagnostics of diseases associated with femurs. Quantitative results are provided using the DICE coefficient and the processing time, on a set of clinical data that indicate the validity of our proposal.

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Acknowledgements

This research project was subsidized by the San Agustín National University. RDE No. 121-2016-FONDECYT-DE, RV. No. 004-2016-VR.INV-UNSA. Thanks to the “Research Center, Transfer of Technologies and Software Development R + D + i” – CiTeSoft-UNSA for their collaboration in the use of their equipment and facilities, for the development of this research work.

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Authors and Affiliations

  1. San Agustín National University of Arequipa, Arequipa, Peru

    Joel Oswaldo Gallegos Guillen, Laura Jovani Estacio Cerquin & Eveling Castro-Gutierrez

  2. Austral University of Chile, Valdivia, Chile

    Javier Delgado Obando

Authors
  1. Joel Oswaldo Gallegos Guillen
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  2. Laura Jovani Estacio Cerquin
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  3. Javier Delgado Obando
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  4. Eveling Castro-Gutierrez
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Corresponding author

Correspondence to Eveling Castro-Gutierrez .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Gallegos Guillen, J.O., Estacio Cerquin, L.J., Obando, J.D., Castro-Gutierrez, E. (2018). Segmentation of the Proximal Femur by the Analysis of X-ray Imaging Using Statistical Models of Shape and Appearance. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-91262-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91261-5

  • Online ISBN: 978-3-319-91262-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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