Multiscale segmentation through a radial basis neural network

This paper presents an approach for image segmentation using sub-octave wavelet representations and a dynamic resource-allocating neural network. The algorithm is applied to identify regions of masses in mammographic images of varied degrees of perceptual difficulty. Each mammographic image is first decomposed into overcomplete wavelet representations of sub-octave frequency bands. A feature vector for each pixel through the scale space is constructed from fine to coarse scales. The feature vectors are used to drive a neural network classifier of dynamic resource allocation for segmentation. Sub-octave wavelet representations have an improved capability of characterizing subtle (band-limited) features frequently seen in mammographic images. A radial basis network of dynamic resource allocation is shown to have better adaptation and generalization in a redundant feature space. Experimental results along with statistical analysis are partially compared to a traditional classifier.