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

  • Joel Oswaldo Gallegos Guillen
  • Laura Jovani Estacio Cerquin
  • Javier Delgado Obando
  • Eveling Castro-GutierrezEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

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.

Keywords

Segmentation AP X-ray Statistical shape models (SSM) Statistical appearance models (SAM) Gold standard DICE coefficient 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.San Agustín National University of ArequipaArequipaPeru
  2. 2.Austral University of ChileValdiviaChile

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