Hybrid fuzzy level set approach for multiple sclerosis lesions assessment in magnetic resonance brain images

Multiple sclerosis is a neurological autoimmune disease characterised by progressive degeneration due to the myelin attack on the central nervous system. The diagnosis is based essentially on clinical features and additional examinations mainly magnetic resonance imaging findings. The diagnosis of multiple sclerosis requires all defined criteria that aim to study spatial and temporal dissemination. Thus, in this work, the automatic segmentation of multiple sclerosis plaques is opted in order to computerise the process and the follow-up. This approach is a hybrid method allowing to combine fuzzy c-means method with geodesic models. This is a retrospective study in which data were collected from the National Institute of Neurology in Tunisia. The eventual results are improved after some pre-treatments. High accuracy was achieved for the models discussed in this paper (93%-84%). Accordingly, the suitability and practical usefulness of the 'simple' pre-treatments to achieve multiple sclerosis classification are demonstrated.