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

doi:10.1007/978-3-319-91262-2_3

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Artificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings
Editors	Ryszard Tadeusiewicz, Leszek Rutkowski, Witold Pedrycz, Rafal Scherer, Marcin Korytkowski, Jacek M. Zurada
Publisher	Springer Verlag
Pages	25-35
Number of pages	11
ISBN (Print)	9783319912615
DOIs	https://doi.org/10.1007/978-3-319-91262-2_3
State	Published - 2018
Event	17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018 - Zakopane, Poland Duration: 3 Jun 2018 → 7 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10842 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018
Country/Territory	Poland
City	Zakopane
Period	3/06/18 → 7/06/18

Bibliographical note

Funding Information:
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.

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)

Access to Document

10.1007/978-3-319-91262-2_3

Cite this

Gallegos Guillen, J. O., Estacio Cerquin, L. J., Obando, J. D., & Castro Gutierrez, E. G. (2018). Segmentation of the proximal femur by the analysis of X-ray imaging using statistical models of shape and appearance. In R. Tadeusiewicz, L. Rutkowski, W. Pedrycz, R. Scherer, M. Korytkowski, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings (pp. 25-35). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10842 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-91262-2_3

Gallegos Guillen, Joel Oswaldo ; Estacio Cerquin, Laura Jovani ; Obando, Javier Delgado et al. / Segmentation of the proximal femur by the analysis of X-ray imaging using statistical models of shape and appearance. Artificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings. editor / Ryszard Tadeusiewicz ; Leszek Rutkowski ; Witold Pedrycz ; Rafal Scherer ; Marcin Korytkowski ; Jacek M. Zurada. Springer Verlag, 2018. pp. 25-35 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018 ; Conference date: 03-06-2018 Through 07-06-2018",

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Gallegos Guillen, JO, Estacio Cerquin, LJ, Obando, JD & Castro Gutierrez, EG 2018, Segmentation of the proximal femur by the analysis of X-ray imaging using statistical models of shape and appearance. in R Tadeusiewicz, L Rutkowski, W Pedrycz, R Scherer, M Korytkowski & JM Zurada (eds), Artificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10842 LNAI, Springer Verlag, pp. 25-35, 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, Zakopane, Poland, 3/06/18. https://doi.org/10.1007/978-3-319-91262-2_3

Segmentation of the proximal femur by the analysis of X-ray imaging using statistical models of shape and appearance. / Gallegos Guillen, Joel Oswaldo; Estacio Cerquin, Laura Jovani; Obando, Javier Delgado et al.
Artificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings. ed. / Ryszard Tadeusiewicz; Leszek Rutkowski; Witold Pedrycz; Rafal Scherer; Marcin Korytkowski; Jacek M. Zurada. Springer Verlag, 2018. p. 25-35 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10842 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Segmentation of the proximal femur by the analysis of X-ray imaging using statistical models of shape and appearance

AU - Gallegos Guillen, Joel Oswaldo

AU - Estacio Cerquin, Laura Jovani

AU - Obando, Javier Delgado

AU - Castro Gutierrez, Eveling Gloria

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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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KW - DICE coefficient

KW - Gold standard

KW - Segmentation

KW - Statistical appearance models (SAM)

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Artificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings

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A2 - Rutkowski, Leszek

A2 - Pedrycz, Witold

A2 - Scherer, Rafal

A2 - Korytkowski, Marcin

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PB - Springer Verlag

T2 - 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018

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ER -

Gallegos Guillen JO, Estacio Cerquin LJ, Obando JD, Castro Gutierrez EG. Segmentation of the proximal femur by the analysis of X-ray imaging using statistical models of shape and appearance. In Tadeusiewicz R, Rutkowski L, Pedrycz W, Scherer R, Korytkowski M, Zurada JM, editors, Artificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings. Springer Verlag. 2018. p. 25-35. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-91262-2_3

Segmentation of the proximal femur by the analysis of X-ray imaging using statistical models of shape and appearance

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