Abstract
In this paper an automatic algorithm for segmentation of bone structures in CT volumes has been developed. This is a complicated task because bones present intensities overlapping with those of surrounding tissues. This overlapping happens because of the presence of some diseases and the different densities present in the bones, providing values similar to those in other tissues like muscle, fat or some organs. In our implementation, gray information and statistical information have been combined to be used as input to a continuous max-flow algorithm to get accurate and fast bone segmentation. Twenty CT images have been automatically segmented and several coefficients such as DICE, Jaccard, Sensitivity and Positive Predictive Value (PPV) indexes have been computed. High sensitivity values above 0.97 were obtained, which shows that the results are promising. Besides, low computational times under 0.6s in the max-flow algorithm were obtained, implying lower times in comparison to many algorithms in the literature.
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Pérez Carrasco, JA., Serrano Gotarredona, C., Suárez-Mejías, C., Acha-Piñero, B. (2014). Statistical-Based Segmentation of Bone Structures via Continuous Max-Flow Optimization. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_23
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DOI: https://doi.org/10.1007/978-3-319-11755-3_23
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