A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI.
Quantitative assessment of Magnetic Resonance Imaging (MRI) lesion load of patients with multiple sclerosis (MS) is the most objective approach for a better understanding of the history of the pathology, either natural or modified by therapies. To achieve an accurate and reproducible quantification of MS lesions in conventional brain MRI, an automatic segmentation algorithm based on a multiresolution approach using pyramidal data structures is proposed. The systematic pyramidal decomposition in the frequency domain provides a robust and flexible low level tool for MR image analysis. Context-dependent rules regarding MRI findings in MS are used as high level considerations for automatic lesion detection.
Pachai
C
C
CREATIS, CNRS Research Unit UMR 5515, INSA 502, Villeurbanne, France. pachai@creatis.insa-lyon.fr
Zhu
Y M
YM
Grimaud
J
J
Hermier
M
M
Dromigny-Badin
A
A
Boudraa
A
A
Gimenez
G
G
Confavreux
C
C
Froment
J C
JC
D000465:Algorithms; D001927:Brain Diseases / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging; D009103:Multiple Sclerosis / Q000473:pathology*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity