Clustering and Feature Selection for Brain Segmentation
Automatic segmentation of brain tissues from MRI is of great importance for clinical application and scientific research. Recent advancements in supervoxel-level analysis enable robust segmentation of brain tissues by exploring the inherent information among multiple features extracted on the supervoxels. Within this prevalent framework, the difficulties still remain in clustering uncertainties imposed by the heterogeneity of tissues and the redundancy of the MRI features.
To cope with the aforementioned two challenges, we propose a robust discriminative segmentation method from the view of information theoretic learning. The prominent goal of the method is to simultaneously select the informative feature and to reduce the uncertainties of supervoxel assignment for discriminative brain tissue segmentation. Experiments on two brain MRI datasets verified the effectiveness and efficiency of the proposed approach.
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