Segmentation techniques for the classification of brain tissue using magnetic resonance imaging.

A technique is described for classifying brain tissue into three components: gray matter, white matter, and cerebrospinal fluid. This technique uses simultaneously registered proton density and T2-weighted images. Samples of each of the three types of tissue are identified on both image sets and used as "training classes"; these tissue samples are then used to generate a linear discriminant function, which is used to classify the remaining pixels in the image data set. Effects of varying the location and number of training classes have been explored; six pairs of training classes have been found to yield a suitable classification. Interrater and test-retest reliability have been examined and found to be good. Intrascanner and interscanner reproducibility has also been evaluated; classification rates are reproducible within the same individual when the same scanner is used, but in this study poor reproducibility occurs when the same individual is scanned on two different scanners. The validity of the technique has been tested by examining correlations between traced and segmented regions of interest, evaluating correlations with age, and conducting phantom studies, in addition to using visual inspection of the classified images as an indication of face validity. From all four perspectives, the method has been found to have good validity. Additional applications, strengths, and limitations are discussed.