Classifiability criteria for refining of random walks segmentation

This paper proposes a novel approach to improve the segmentation quality of a 3D random walks algorithm using classifiability criteria. We produce a range of potential threshold values by extending the decision function of a random walks algorithm using a likelihood ratio test. Optimal threshold values are quantitatively isolated using two data-driven methods: maximum total accuracy and Bayesian cross validation criteria. The proposed methods are evaluated using a dataset of 28 dental lesions in 3D cone-beam CT scans. Both methods produce viable thresholds, the first corresponding to a conservative segmentation and the second a relaxed segmentation. We qualitatively compare the results to determine the best method.