Analyzing medical images, which have been stored in digital information system day by day, is expected to make it possible to formulate knowledge useful for image diagnosis. In this paper, we propose a method for unsupervised medical image segmentation as the pre-processing of the analysis aiming to clear the relation between the image features and the possible outcome of a medical condition. In the proposed method, a square region around the every pixel is considered as a pattern vector, and a set of pattern vectors acquired from whole image are classified by using the technique of hierarchical clustering. In the hierarchical clustering, the set of pattern vectors is divided into two clusters at each node, according to the statistical criterion based on the entropy in thermodynamics. Results on the test image generated by the Markov Random Field model and the real medical images, photomicrographs of intestine, are shown.