Segmentation of Brain Tissue from MR Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. In this paper, we present a method for segmentation of a particularly complex structure, the brain tissue, from MRI. Our method is a combination of three existing techniques from the Computer Vision literature: adaptive segmentation, binary morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation from gradient echo images, and the resultant method is more robust than its components. We present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 10 brains each with 256x256x124 voxels.