Unsupervised algorithm for the segmentation of three-dimensional magnetic resonance brain images

This paper presents a multiple resolution algorithm for the segmentation of three-dimensional magnetic resonance (MR) images. The algorithm consists in the unsupervised segmentation of the MR volume into regions of different statistical behavior. Firstly, an unsupervised merging algorithm estimates a block segmentation of the volume while determining the region number and the parameters of those regions. This estimation is computed by minimizing a global information criterion. Next, the small regions are eliminated using statistic criteria. Finally, the segmentation is performed using the neighboring relationships between voxels via hidden Markov random fields and a multiple resolution iterated conditional mode algorithm. Some results on volumetric brain MR images are presented and discussed