Adaptable fuzzy C-Means for improved classification as a preprocessing procedure of brain parcellation

Parcellation, one of several brain analysis methods, is a procedure popular for subdividing the regions identified by segmentation into smaller topogr aphically defined units. The fuzzy clustering algorithm is mainly used to p reprocess parcellation into several segmentation methods, because it is ver y appropriate for the characteristics of magnetic resonance imaging (MRI), such as partial volume effect and intensity inhomogeneity. However, some gr ay matter, such as basal ganglia and thalamus, may be misclassified into th e white matter class using the conventional fuzzy C-Means (FCM) algorithm. Parcellation has been nearly achieved through manual drawing, but it is a t edious and time-consuming process. We propose improved classification using successive fuzzy clustering and implementing the parcellation module with the modified graphic user interface (GUI) for the convenience of users. Cop yright (C) 2001 by W.B. Saunders Company.