A multiscale approach to automatic medical image segmentation using self-organizing map

In this paper, a new medical image classification scheme is proposed using self-organizing map (SOM) combined with multiscale technique. It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers. First, to solve the difficulty in manual selection of edge pixels, a multiscale edge detection algorithm based on wavelet transform is proposed. Edge pixels detected are then selected into the training set as a new class and a multiscale SOM classifier is trained using this training set. In this new scheme, the SOM classifier can perform both the classification on the entire image and the edge detection simultaneously. On the other hand, the misclassification of the traditional multiscale SOM classifier in regions near edges is greatly reduced and the correct classification is improved at the same time.