A level set method based on the Bayesian risk for medical image segmentation |
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Authors: | Yao-Tien Chen [Author Vitae] |
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Affiliation: | Department of Computer Science and Information Engineering, Yuanpei University, No. 306, Yuanpei St., Hsinchu City 30015, Taiwan |
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Abstract: | This paper proposes an alternative criterion derived from the Bayesian risk classification error for image segmentation. The proposed model introduces a region-based force determined through the difference of the posterior image densities for the different classes, a term based on the prior probability derived from Kullback-Leibler information number, and a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. Compared with other level set methods, the proposed approach relies on the optimum decision of pixel classification and the estimates of prior probabilities; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach is able to extract the complicated shapes of targets and robust for various types of medical images. Moreover, the algorithm can be easily extendable for multiphase segmentation. |
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Keywords: | Level set method Bayesian risk Hypothesis test Prior probability Kullback-Leibler information number |
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