PURPOSE: A new multistep, volumetric-based tissue segmentation algorithm that results in fuzzy (or probabilistic) voxel description is described. This algorithm is designed to accurately segment gray matter, white matter, and CSF and can be applied to both single channel high resolution and multispectral (multiecho) MR images. METHOD: The reliability and validity of this method are evaluated by assessing (a) the stability of the algorithm across time, rater, and pulse sequence; (b) the accuracy of the method when applied to both real and synthetic image datasets; and (c) differences in specific tissue volumes between individuals with a specific genetic condition (fragile X syndrome) and normal control subjects. RESULTS: The algorithm was found to have high reliability, accuracy, and validity. The finding of increased caudate gray matter volume associated with the fragile X syndrome is replicated in this sample. CONCLUSION: Since this segmentation approach incorporates "fuzzy" or probabilistic methods, it has the potential to more accurately address partial volume effects, anatomical variation within "pure" tissue compartments, and more subtle changes in tissue volumes as a result of disease and treatment. The method is a component of software that is available in the public domain and has been implemented on an inexpensive personal computer thus offering an attractive and promising method for determining the status and progression of both normal development and pathology of the CNS.
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