A Novel Algorithm for Automatic Brain Structure Segmentation from MRI

This paper proposes an automatic segmentation algorithm that combines clustering and deformable models. First, a k-means clustering is performed based on the image intensity. A hierarchical recognition scheme is then used to recognize the structure to be segmented, and an initial seed is constructed from the recognized region. The seed is then evolved under certain deformable model mechanism. The automatic recognition is based on fuzzy logic techniques. We apply our algorithm for the segmentation of the corpus callosum and the thalamus from brain MRI images. Depending on the specific features of the segmented structures, the most suitable recognition schemes and deformable models are employed. The whole procedure is automatic and the results show that this framework is fast and robust.