Unsupervised image segmentation using hierarchical clustering

Abstract

In the analysis of a medical image database aimed at formulating useful knowledge for image diagnosis requires an unsupervised image processing technique without preconceived knowledge. In this paper, we propose a method for unsupervised image segmentation, which is imitable for finding the features contained in an image. A small region around each pixel is considered as a pattern vector, and the set of pattern vectors acquired from the whole image is classified using the hierarchical clustering technique. In hierarchical clustering, the classification of pattern vectors is divided into two clusters at each node according to the statistical criterion based on the entropy in thermodynamics. Results of the test image generated by the Markov random field (MRF) model and real medical images photomicrographs of a colon tumor are shown.