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 Gloria Castro Gutierrez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaArtificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings
EditoresRyszard Tadeusiewicz, Leszek Rutkowski, Witold Pedrycz, Rafal Scherer, Marcin Korytkowski, Jacek M. Zurada
EditorialSpringer Verlag
Páginas25-35
Número de páginas11
ISBN (versión impresa)9783319912615
DOI
EstadoPublicada - 2018
Evento17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018 - Zakopane, Polonia
Duración: 3 jun. 20187 jun. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10842 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018
País/TerritorioPolonia
CiudadZakopane
Período3/06/187/06/18

Nota bibliográfica

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

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