Exploiting the self-organizing map for medical image segmentation

As the computer technology advances, data acquisition, processing and visualization techniques have a tremendous impact on medical imaging. On the other hand, however, the interpretation of medical images is still almost performed by radiologists nowadays. Developments in artificial intelligence and image processing show that computer-aided diagnosis emerges with increasingly high potential. In this paper, we develop an intelligent approach to perform image segmentation and thus to discover region of interest (ROI) for diagnosis purposes through the use of self-organizing map (SOM) techniques. Specifically, we propose a two-stage SOM approach which can precisely identify dominant color components and thus segment a medical image into several smaller pieces. In addition, with a proper merging step conducted iteratively, one or more ROIs in a medical image can usually be identified. Empirical studies show that our approach is effective at processing various types of medical images. Moreover, the feasibility of our approach is also evaluated by the illustration of image semantics.