Abstract: To improve the segmentation performance of medical images, dictionary learning was combined with clustering algorithm, and a medical image segmentation algorithm was proposed taking dictionaries as clustering centers and using sparse representation to cluster for segmentation. For a single medical image, unsupervised adaptive segmentation can be achieved by alternately iterating the sparse coding and updating the dictionary to convergence. For the medical image sequence, the sample images can be picked to obtain the trained dictionaries to complete the segmentation of the image sequence. According to the segmentation results of the synthetic images and the magnetic resonance images of the human brain from SBD database, it can be perceived that the proposed algorithm could not only improve segmentation accuracy, but also maintain the accuracy and consistency of sequential medical image segmentation.

Key words: clustering, dictionary learning, sparse representation, medical image segmentation