Feature-Based Deformable Registration Using Minimal Spanning Tree for Prostate MR Segmentation

Automatic and accurate segmentation of the prostate is still a challenging task due to intensity inhomogeneity and complicated deformation of MR images. To tackle these problems with multi-atlas segmentation, in this paper, we propose a new metric for image registration and new descriptor for label fusion. First, to reduce the amount of edges in entropic graph, a modified ff-mutual information (alpha-MI) based on fast minimal spanning tree (MST) is implemented for deformable registration. Second, localized alpha-MI allowing for the spatial information is proposed with the stochastic gradient optimization, and the feature space is encoded by a sparse auto-encoder. Finally, a multi-scale descriptor utilizing local self-similarity is integrated into the patch-based label fusion to obtain final segmentation. Experiments were performed on two subsets of totally 46 T2-weighted prostate MR images from 46 patients. Compared to alpha-MI based on k-nearest neighbor graph, the registration time of alpha-MI based on fast MST can be reduced by almost half. The median Dice overlap of registration using localized alpha-MI on one subset is shown to improve significantly from 0.725 to 0.764 (p = 1:14 x 10(-5)), compared to using alpha-MI without the spatial information. The median Dice overlap of prostate segmentation using the proposed method on 20 testing images of another subset is 0.871, and the median Hausdorff distance is 8.013 mm, which demonstrate a comparable accuracy to state-of-the-art methods.