@article{oai:kanagawa-u.repo.nii.ac.jp:00006937,
 author = {Zhang, Shanjun and 張, 善俊 and Yoshino, Kazuyoshi and 吉野, 和芳},
 journal = {Science Journal of Kanagawa University},
 month = {May},
 note = {The interior boundary of medical image is fuzzy in nature. In this paper, proposed is a novel method to segment and classify the MR image of head by fuzzy clustering and fuzzy reasoning. Traditional fuzzy clustering methods are basically statistical ones in which only intensity affinities of the image are reflected. Considering the characteristics of MR image, we constructed a set of knowledge-based rules to set the fuzzy memberships of the pixels of the image by generally using the intensity similarities, positional relationships among multiple spectra MR images, and the shape features of the brain tissues and the mathematics morphological analogy of the brain tissues. Then a coarse-to-fine reasoning method is used to combine the fuzzy memberships of the pixels of the T1- and T2- channels of the image to segment the cerebral tissues into gray matter, white matter, and CSF. Experimental results showed the efficiency of the method.},
 pages = {3--10},
 title = {A Rule-Based Expert System for Automatic Segmentation of Cerebral MRI Images},
 volume = {18},
 year = {2007}
}