Experiments in the automated detection of multiple sclerosis brain lesions in magnetic resonance images

Summary form only given. Artificial intelligence techniques of machine learning, pattern recognition, and the use of domain knowledge were employed in the segmentation, or automated detection, of multiple sclerosis (MS) lesions in magnetic resonance images of the human brain. The performances of the statistical minimum distance and Bayesian classifiers, applied to MS lesion segmentation, are compared to that of the classifiers developed by pruned and unpruned decision tree learning. The statistical classifiers were the fastest in training, yet were the slowest in recall. Each classifier performed at about the same level of accuracy. An additional difference is seen in the interpretability of each classifier's learned rules. Whereas the minimum distance and Bayesian classifiers represent class descriptions with mathematical formulas, the decision tree classifier's representation of acquired knowledge is symbolic. Classification rules produced by the pruned decision tree classifier were concise, and thus preferable for their human interpretability.<
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