A neural network approach to unsupervised segmentation of single-channel MR images

A novel neural network-based technique for segmentation of single-channel magnetic resonance images is presented. The segmentation of single-channel magnetic resonance images is a daunting task due to the relatively little information available at each pixel site. The proposed algorithm is based on unsupervised clustering by means of a Kohonen Self-Organizing Map: unsupervised segmentation algorithms are highly desirable in order to eliminate intra- and interobserver variability. Particular attention has been devoted to the choice of suitable features, in order to ensure an accurate and reliable segmentation: in particular, a feature set extracted from the neighborhood of each pixel has been evaluated. The proposed technique has been tested on simulated magnetic resonance images to assess its stability against the presence of noise and intensity inhomogeneities. Moreover, it has been tested on real magnetic resonance images of both volunteers and brain tumor patients. The preliminary results presented make the proposed technique a promising alternative for the segmentation of single-channel magnetic resonance images and encourage further investigation.