Adaptive Segmentation of MR Axial Brain Images Using Connected Components
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
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In MR brain images, segmentation using intensity values is severely limited owing to field inhomogeneities, susceptibility artifacts and partial volume effects. Edge based segmentation methods suffer from spurious edges and gaps in boundaries. A method is presented which combines the advantages of edge based and region based segmentation. First a multiscale image representation is constructed which favors intra-tissue diffusion over inter-tissue diffusion by exploiting local contrast. Subsequently a multiscale linking model (the hyperstack) is used to group voxels into a number of segments. This facilitates segmentation of grey matter, white matter and cerebrospinal fluid with minimal user interaction. Using a supervised segmentation technique and MR simulations of a brain phantom as validation it is shown that the errors are in the order of or smaller than reported in literature.