Abstract: We present development and application of a feature extraction method for magnetic resonance imaging (MRI), without explicit calculation of tissue parameters. We generate a three-dimensional (3-D) feature space representation of the data, in which normal tissues are clustered around pre-specified target positions and abnormalities are clustered somewhere else. This is accomplished by a linear minimum mean square error transformation of categorical data to target positions. From the 3-D histogram (cluster plot) of the transformed data, we identify clusters and define regions of interest (ROIs) for normal and abnormal tissues. There ROIs are used to estimate signature (feature) vectors for each tissue type which in turn are used to segment the MRI scene. The proposed feature space is compared to those generated by tissue-parameter-weighted images, principal component images, and angle images, demonstrating its superiority for feature extraction. The method and its performance are illustrated using a computer simulation and MRI images of an egg phantom and a human brain.!19
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