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-Gutierrez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationArtificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings
EditorsRyszard Tadeusiewicz, Leszek Rutkowski, Witold Pedrycz, Rafal Scherer, Marcin Korytkowski, Jacek M. Zurada
PublisherSpringer Verlag
Pages25-35
Number of pages11
ISBN (Print)9783319912615
DOIs
StatePublished - 2018
Event17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018 - Zakopane, Poland
Duration: 3 Jun 20187 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10842 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018
Country/TerritoryPoland
CityZakopane
Period3/06/187/06/18

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

Keywords

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

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