title: Classification of multiple sclerosis lesions using adaptive dictionary learning.
abstract: This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification.
authors.affiliation: University of Rennes 1, Faculty of Medicine, F-35043 Rennes, France; INSERM, U746, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; Inria, VISAGES Project-Team, F-35042 Rennes, France. Electronic address: hrishikesh.deshpande@inria.fr.
authors.forename: Hrishikesh
authors.initials: H
authors.lastname: Deshpande
authors.affiliation: University of Rennes 1, Faculty of Medicine, F-35043 Rennes, France; INSERM, U746, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; Inria, VISAGES Project-Team, F-35042 Rennes, France. Electronic address: pierre.maurel@irisa.fr.
authors.forename: Pierre
authors.initials: P
authors.lastname: Maurel
authors.affiliation: University of Rennes 1, Faculty of Medicine, F-35043 Rennes, France; INSERM, U746, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; Inria, VISAGES Project-Team, F-35042 Rennes, France. Electronic address: christian.barillot@irisa.fr.
authors.forename: Christian
authors.initials: C
authors.lastname: Barillot
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004014:Dictionaries as Topic; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D009103:Multiple Sclerosis / Q000145:classification / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods
keywords: Adaptive dictionary learning; Computer aided diagnosis; Magnetic resonance imaging; Sparse representations