title: Fuzzy approach toward reducing false positives in the detection of small multiple sclerosis lesions in magnetic resonance images.
abstract: The large number of false positives that result when automatic algorithms are considered for segmenting small multiple sclerosis lesions in magnetic resonance imaging hampers the posterior evaluation of lesion load. To address this problem we propose a fuzzy system which can improve the differentiation between true and false positive detections in proton density- and T2-weighted images. On the basis of an earlier work, which was focused on the detection of hyperintense regions in MR brain images, the system here presented introduces fuzzy restrictions derived from the regional analysis of the main features in such regions. Results show a reduction to a 3.6% in the number of false detections while preserving most of the true detections obtained using previous algorithm.
authors.affiliation: Magnetic Resonance Unit - IDI, Vall Hebron University Hospital, E-08035 Barcelona, Spain. xavier.aymerich@idi-cat.org
authors.forename: F X
authors.initials: FX
authors.lastname: Aymerich
authors.forename: P
authors.initials: P
authors.lastname: Sobrevilla
authors.forename: E
authors.initials: E
authors.lastname: Montseny
authors.forename: A
authors.initials: A
authors.lastname: Rovira
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D005189:False Positive Reactions; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity