A method for fully automated measurement of neurological structures in MRI

A method for fully automating the measurement of various neurological structures in MRI is presented. This technique uses an atlas-based trained maximum likelihood classifier. The classifier requires a map of prior probabilities, which is obtained by registering a large number of previously classified data sets to the atlas and calculating the resulting probability that each represented tissue type or structure will appear at each voxel in the data set. Classification is then carried out using the standard maximum likelihood discriminant function, assuming normal statistics. The results of this classification process can be used in three ways, depending on the type of structure that is being detected or measured. In the most straightforward case, measurement of a normal neural substructure such as the hippocampus, the results of the classifier provide a localization point for the initiation of a deformable template model, which is then optimized with respect to the original data. The detection and measurement of abnormal structures, such as white matter lesions in multiple sclerosis patients, requires a slightly different approach. Lesions are detected through the application of a spectral matched filter to areas identified by the classifier as white matter. Finally, detection of unknown abnormalities can be accomplished through anomaly detection.