During initial segmentation, the FCM algorithm is used to cluster the input slice into ten classes. As a result, each FCM class will have a cluster center in . We ignore some tissues and vascular structures as not of interest in this work.
Air appears in the lowest one or two classes as separate classes.
For the selected type of tumors, tumor appears in the highest or the second highest T2 class. CSF takes the highest T2 class in the absence of tumors, and the highest or second highest T2 class in the presence of tumors. These phenomena are stable throughout each slice in the brain volume for the particular RF pulse sequence used and provide valuable information that allows us to quickly remove extraneous data, such as the clusters representing air and skull tissue, and provide help in labeling the remaining clusters.