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Medical image clustering algorithm based on graph entropy

Recently, a variety of medical imaging technologies have been used widely in clinical diagnosis. As a large number of medical images are produced everyday, it becomes a hot issue of data mining on medical image in current that how to make full use of these medical images and cluster efficiently to help doctors to diagnose. In this paper, we propose a medical image clustering method. Firstly, medical image dataset is represented as a weighted, undirected and completed graph. Secondly, the graph is sparsified and pruned. This model can describe the similarity between medical images very well. Last, weighted and undirected graph clustering method based on graph entropy is proposed to cluster these medical images. The experimental results show that this method can cluster medical images efficiently and run well in time complexity and clustering results.