Biomedical image processing

Describes a possible model of human perceptive process. In the work the relation between the entropy of an image domain and the entropy of its subdomain is explored as uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of grey levels. With the aim to implement the model the authors have introduced a well known technique of problem solving. The most important roles of the authors' model are played by the evaluation function (EF) and the control strategy. So the EF is related to the ratio between the entropy of one region or zone of the picture and the entropy of entire picture. The control strategy determines the optimal path in the search tree (quadtree) so that the nodes of the optimal path have minimal entropy. The paper shows some comparisons among different evaluation functions in the segmentation process of biomedical pictures using liver CT as an example.