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Multiresolution Mesh Segmentation of MRI Brain Using Classification and Discrete Curvature

This paper presents a method for brain tissue segmentation and characterization of magnetic resonance imaging (MRI) scans. It is based on statistical classification, differential geometry, and multiresolution representation. The Expectation Maximization algorithm and k-means clustering are applied to generate an initial mask of tissue classes of data volume. Then, a hierarchical multiresolution representation is applied to simplify processing. The idea is that the low-resolution description is used to determine constraints for the segmentation at the higher resolutions. Our contribution is the design of a pipeline procedure for brain characterization/labeling by using discrete curvature and multiresolution representation. We have tested our method on several MRI data.