Paper Info
Title
An a contrario approach for outliers segmentation: Application to Multiple Sclerosis in MRI
Abstract
The detection of Multiple Sclerosis (MS) lesions in Magnetic Resonance (MR) images remains an important issue in medical image processing. Diagnostic criteria for MS based on brain MRI concern mainly dissemination in space and time. In this context, this paper describes a novel region- based approach to automatically count the number of MS lesions present in a set of MR images. Given a set of candidate regions obtained with a mean-shift based segmentation, the detection algorithm decides for each region if it is part of a MS lesion or if it belongs to non-pathologic regions (white matter (WM), grey matter (GM) or cerebro-spinal fluid (CSF)). The distribution of each brain tissue is modeled using a Gaussian Mixture Model and MS lesions are detected as outliers with respect to this model. Finally, we propose several criteria for segmentation assessment and we validate our algorithm on the Brain Web data set. Preliminary results on clinical data are also shown.
Year
DOI
Venue
2008
10.1109/ISBI.2008.4540919
Paris
Keywords
Field
DocType
Gaussian distribution,biological tissues,biology computing,biomedical MRI,brain,cellular biophysics,image segmentation,medical image processing,Brain Web data set,Gaussian mixture model,MRI,brain tissue,cerebro-spinal fluid,contrario approach,detection algorithm,grey matter,mean-shift based segmentation,medical image processing,multiple sclerosis lesions,white matter,Brain Segmentation,MRI,Multiple Sclerosis Lesions,a contrario Framework
Brain segmentation,Computer vision,Grey matter,Pattern recognition,Segmentation,Computer science,Outlier,Image processing,Image segmentation,Artificial intelligence,Mixture model,Magnetic resonance imaging
Conference
ISSN
ISBN
Citations 
1945-7928
978-1-4244-2003-2
7
PageRank 
References 
Authors
0.75
9
4
Name
Order
Citations
PageRank
Francois Rousseau112116.81
Blanc, F.270.75
de Seze, J.370.75
Rumbach, L.4171.43