Case-based Reasoning for Image Segmentation by Watershed Transformation

This chapter introduces a novel image-segmentation scheme based on case-based reasoning. Image segmentation is aimed at dividing an image into a number of different regions in such a way that each region is homogeneous with respect to a given property, but the union of any two adjacent regions is not. To reach this goal, a number of different approaches have been suggested in the literature, among which we consider here watershed-based segmentation. The basic idea of this segmentation scheme is to identify in the gray-level image a suitable set of seeds from which to perform a growing process. The growing process groups to each seed all pixels that are closer to that seed more than to any other seed, provided that a certain homogeneity condition is satisfied. Since any segmentation method includessome parameters, whose values depend on the image characteristics, CBR can be profitably used to improve the performance of the adopted segmentation method and to ensure that good segmentation results are obtained even if the segmentation method is applied to images with different characteristics. In practice, CBR will select from a case-base the cases having image characteristics similar to those of the current input image, and will apply to the current image the segmentation parameters associated to the most similar case. Image characteristics will be computed in terms of mean features on the whole image, and a proper similarity measure will be used to select in the case-base the most similar case.