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.
|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|
|Number of pages||11|
|State||Published - 2018|
|Event||17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018 - Zakopane, Poland|
Duration: 3 Jun 2018 → 7 Jun 2018
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018|
|Period||3/06/18 → 7/06/18|
Bibliographical noteFunding 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.
© Springer International Publishing AG, part of Springer Nature 2018.
- AP X-ray
- DICE coefficient
- Gold standard
- Statistical appearance models (SAM)
- Statistical shape models (SSM)