Learning to identify fuzzy regions in magnetic resonance images

Abstract

The paper presents an approach to automatic heuristic rule generation for tissue labeling in a magnetic resonance (MR) volumetric image of the human brain. The image is clustered with the semi-supervised fuzzy c-means (ssFCM) algorithm. The clusters are then labeled by analyzing the membership of pixels in the cluster and the corresponding ground truth data. Finally, production rules which are capable of labeling unseen data are learned. Production rule cluster type identification error rates decrease as the clusters become more homogeneous. After imposing a minimum of 70% cluster homogeneity on both the training and the testing data sets, this system was tested using 10-fold cross validation on 29 normal slices with an average cluster type identification error rate of 1.2%.