Segmentation of multispectral MR images using a hierarchical self-organizing map

The application of a hierarchical self-organizing map (HSOM) to the problem of segmentation of multispectral magnetic resonance (MR) images is investigated. The HSOM is composed of several layers of self-organizing maps (SOMs) organized in a pyramidal fashion. SOMs have previously been used for the segmentation of multispectral MR images, but the results often suffer from under-segmentation or over-segmentation. By combining the concepts of self-organization and topographic mapping with multi-scale image segmentation, the HSOM is shown to overcome the major drawbacks of the SOM. The segmentation results of the HSOM are compared with those of the SOM and the k-means clustering algorithm on multispectral MR images of the human brain representing both normal conditions and pathological conditions, such as multiple sclerosis. The multi-scale segmentation results of the HSOM are shown to have interesting consequences from the viewpoint of the clinical diagnosis of pathological conditions