Segmentation of the Proximal Femur by the Analysis of X-ray Imaging Using Statistical Models of Shape and Appearance
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.
KeywordsSegmentation AP X-ray Statistical shape models (SSM) Statistical appearance models (SAM) Gold standard DICE coefficient
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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