A multiresolution probabilistic neural network for image segmentation
Stefanos Kollias, Dimitris Kalogeras · 2002
A multiresolution network for segmenting textures and magnetic resonance images is proposed, based on maximum likelihood estimation. The network incorporates a probabilistic neural architecture to facilitate the generation of likelihood estimates. Further on, an iterative segmentation process is used, which refines the likelihood estimates based upon both the neighbouring estimated likelihoods and the confidence on these estimates. A multiresolution neural network structure which permits a significant reduction of the time needed to solve the segmentation problem is proposed. This is performed by an initial segmentation at lower resolution and subsequent refinement at higher resolutions.>