Abstract: It is difficult to represent and cluster medical image data by mathematic model. In order to address this problem, an medical image clustering analysis method based on approximate density function is designed. This method uses kernel density estimation model to construct the approximate density function, and takes hill climbing strategy to extract clustering patterns. Results of experiments show that it can achieve good effect on real human abdomen medical images.