Segmentation of bone structures in 3D CT images based on continuous max-flow optimization

J. A. Pérez-Carrasco; B. Acha-Piñero; C. Serrano

doi:10.1117/12.2082139

20 March 2015 Segmentation of bone structures in 3D CT images based on continuous max-flow optimization

J. A. Pérez-Carrasco, B. Acha-Piñero, C. Serrano

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94133Y (2015) https://doi.org/10.1117/12.2082139
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max-flow algorithm. Twenty CT images have been tested and different coefficients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.

Citation Download Citation

J. A. Pérez-Carrasco, B. Acha-Piñero, and C. Serrano "Segmentation of bone structures in 3D CT images based on continuous max-flow optimization", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133Y (20 March 2015); https://doi.org/10.1117/12.2082139

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