title: Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding.
abstract: Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches.
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Mariano
authors.initials: M
authors.lastname: Cabezas
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Eloy
authors.initials: E
authors.lastname: Roura
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Jordi
authors.initials: J
authors.lastname: Freixenet
authors.affiliation: Girona Magnetic Resonance Center, Spain.
authors.forename: Joan C
authors.initials: JC
authors.lastname: Vilanova
authors.affiliation: Multiple Sclerosis and Neuroimmunology Unit, Dr. Josep Trueta University Hospital, Spain.
authors.forename: Lluís
authors.initials: L
authors.lastname: Ramió-Torrentà
authors.affiliation: Magnetic Resonance Unit, Department of Radiology, Vall d'Hebron University Hospital, Spain.
authors.forename: Alex
authors.initials: A
authors.lastname: Rovira
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain. Electronic address: llado@eia.udg.edu.
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
mesh_terms: D000465:Algorithms; D001331:Automation; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000175:diagnosis* / Q000473:pathology*; D016011:Normal Distribution; D011336:Probability; D015203:Reproducibility of Results; D012984:Software
keywords: Lesion segmentation; MRI; Multiple sclerosis