Intensity-adaptive segmentation of single-echo T1-weighted magnetic resonance images

A procedure for segmentation of intracranial tissues, including cerebrospinal fluid surrounding the brain, cortical and subcortical gray matter, and white matter, in a T1-weighted magnetic resonance image of the brain, has been developed. The proposed method utilizes information from the histogram of pixel intensities of the intracranial image. Based on this information, an unsupervised K-means clustering procedure separates various tissue regions. Information about the approximate location of anatomical regions within the intracranial space is used to detect ventricles and the caudate nuclei. First a description and justification for the procedure is presented. Then the performance of the procedure is evaluated by analysis of variance. In conclusion, the results of applying this procedure to 31 healthy subjects are presented and future improvements are discussed.