Abstract
A method has been developed that substantially increases quantitative tissue discrimination of pathology in in-vivo magnetic resonance (MR) imaging. Results from a study of carcinoma and benign hypertrophy of the prostate show the potential for classification of tissues from individual patients and demonstrate increased class separation. This appears to be the firstapplication of pattern recognition techniques to relaxation-time data from in-vivo MR images. Multiple-point relaxation-time maps were classified using both statistical measures and texture descriptors derived from spatial grey level dependency matrices. These parameters were combined in a new pattern-recognition method based upon the Karhunen- Loeve expansion.
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