Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering.
A method is presented for fully automated detection of Multiple Sclerosis (MS) lesions in multispectral magnetic resonance (MR) imaging. Based on the Fuzzy C-Means (FCM) algorithm, the method starts with a segmentation of an MR image to extract an external CSF/lesions mask, preceded by a local image contrast enhancement procedure. This binary mask is then superimposed on the corresponding data set yielding an image containing only CSF structures and lesions. The FCM is then reapplied to this masked image to obtain a mask of lesions and some undesired substructures which are removed using anatomical knowledge. Any lesion size found to be less than an input bound is eliminated from consideration. Results are presented for test runs of the method on 10 patients. Finally, the potential of the method as well as its limitations are discussed.
Boudraa
A O
AO
L2T1, Institut Galilèe, Université Paris 13, Villetanneuse, France. abdel.boudra@l2ti.univ-paris13.fr
Dehak
S M
SM
Zhu
Y M
YM
Pachai
C
C
Bao
Y G
YG
Grimaud
J
J
D000465:Algorithms; D003951:Diagnostic Errors / Q000517:prevention & control; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging; D009103:Multiple Sclerosis / Q000175:diagnosis*; D015203:Reproducibility of Results