A COMPARISON OF NEURAL NETWORK AND FUZZY CLUSTERING-TECHNIQUES IN SEGMENTING MAGNETIC-RESONANCE IMAGES OF THE BRAIN

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzzy-c-means approaches being slightly preferred over feedforward cascade correlation results after several iterations in the selection of training regions. We compare various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process. Each approach seems to provide useful information about different aspects of the problem.