Automatic liver MR image segmentation with self-organizing map and hierarchical agglomerative clustering method

Abstract

Medical image segmentation plays an important role in medical visualization and diagnosis. We study in this paper an automatic segmentation method for liver magnetic resonance (MR) images based on the self-organizing map (SOM) and hierarchical agglomerative clustering method. At first, the local features of the MR image pixels are extracted to feed the SOM after a pre-processing step. The output prototypes are then filtered with the hits map and a hierarchical agglomerative clustering method is applied to the prototypes to select the best segmentation according to a quantitative image evaluation index. The segmentation results after the post-processing show the proposed method to be effective and promising. Further research work is also recommended.