Segmentation of medical images through competitive learning

In image analysis applications, segmentation of gray-level images into meaningful regions is an important low-level processing step. Various approaches to segmentation investigated in the literature, in general, use either local information of gray-level values of pixels (region growing based methods, for example) or the global information (histogram threshold based methods, for example). Application of these approaches for segmenting medical images often does not provide satisfactory results. Medical images are usually characterized by low local contrast and noisy or faded features causing unacceptable performance of local information based segmentation methods. In addition, because of a large amount of structural information found in medical images, global information based segmentation methods yield inadequate results in region extraction. We present a novel approach to image segmentation that combines local contrast as well as global gray-level distribution information. The presented method adaptively learns useful features and regions through the use of a normalized contrast function as a measure of local information and a competitive learning based method to update region segmentation incorporating global information about the gray-level distribution of the image. In this paper, we present the framework of such a self organizing feature map, and show the results on simulated as well as real medical images.