title: FUZZY C-MEANS WITH VARIABLE COMPACTNESS.
abstract: Fuzzy c-means (FCM) clustering has been extensively studied and widely applied in the tissue classification of biomedical images. Previous enhancements to FCM have accounted for intensity shading, membership smoothness, and variable cluster sizes. In this paper, we introduce a new parameter called "compactness" which captures additional information of the underlying clusters. We then propose a new classification algorithm, FCM with variable compactness (FCMVC), to classify three major tissues in brain MRIs by incorporating the compactness terms into a previously reported improvement to FCM. Experiments on both simulated phantoms and real magnetic resonance brain images show that the new method improves the repeatability of the tissue classification for the same subject with different acquisition protocols.
authors.affiliation: Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University.
authors.forename: Snehashis
authors.initials: S
authors.lastname: Roy
authors.forename: Harsh
authors.initials: H
authors.lastname: Agarwal
authors.forename: Aaron
authors.initials: A
authors.lastname: Carass
authors.forename: Ying
authors.initials: Y
authors.lastname: Bai
authors.forename: Dzung L
authors.initials: DL
authors.lastname: Pham
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince