PROPAGATING DISTRIBUTIONS FOR SEGMENTATION OF BRAIN ATLAS

We present a novel method for segmentation of anatomical structures in histological data. Segmentation is carried out slice-by-slice where the successful segmentation of one section provides a prior for the subsequent one. Intensities and spatial locations of the region of interest and the background are modeled by three-dimensional Gaussian mixtures. This information adaptively propagates across the sections. Segmentation is inferred by minimizing a cost functional that enforces the compatibility of the partitions with the corresponding models together with the alignment of the boundaries with the image gradients. The algorithm is demonstrated on histological images of mouse brain. The segmentation results compare well with manual segmentation.