Abstract
This paper presents an effective combination of a statistical atlas-based approach and a graph cuts algorithm for fully automated robust and accurate segmentation. Major contribution of this paper is proposal of two new submodular energies for graph cuts. One is shape constrained energy derived from a statistical atlas based segmentation and the other is for constraint from a neighbouring structure. The effectiveness of the proposed energies was demonstrated using a synthesis image with different errors in shape estimation and clinical CT volumes of liver and lung.
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Shimizu, A. et al. (2011). Automated Segmentation of 3D CT Images Based on Statistical Atlas and Graph Cuts. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2010. Lecture Notes in Computer Science, vol 6533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18421-5_21
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DOI: https://doi.org/10.1007/978-3-642-18421-5_21
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