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

Authors: Chaima Dachraoui; Aymen Mouelhi; Cyrine Drissi; Salam Labidi

Addresses: Research Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, 1006, Tunis, Tunisia ' Laboratory of Signal Image and Energy Mastery, LR13ES03 (SIME), ENSIT, Tunis University, Tunis, Tunisia ' Faculty of Medicine of Tunis, National Institute of Neurology Mongi Ben Hmida, University of Tunis El Manar, Tunis, Tunisia ' Research Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, 1006, Tunis, Tunisia

Abstract: 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.

Keywords: multiple sclerosis; brain; magnetic resonance imaging; MRI; automatic segmentation; hybrid approach; geodesic contour; fuzzy c-means; FCM; lesions segmentation; T2 FLAIR.

DOI: 10.1504/IJMIC.2022.125065

International Journal of Modelling, Identification and Control, 2022 Vol.40 No.3, pp.260 - 270

Received: 17 Jun 2021
Accepted: 24 Sep 2021
Published online: 25 Aug 2022 *

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