University of Oulu

Zouaoui, H., Moussaoui, A., Oussalah, M., & Taleb-Ahmed, A. (2019). A Robust Method for MR Image Segmentation and Multiple Scleroses Detection. Journal of Medical Imaging and Health Informatics, 9(6), 1119–1130.

A robust method for MR image segmentation and multiple scleroses detection

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Author: Zouaoui, H.1; Moussaoui, A.1; Oussalah, M.2;
Organizations: 1Computer Science Department, Ferhat Abbas University, Algeria
2Centre for Ubiquitous Computing, Faculty of Information Technology, University of Oulu, Finland
3LAMIH Laboratory University of Valenciennes France
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: American Scientific Publishers, 2019
Publish Date: 2020-04-23


In the present article, we propose a new approach for the segmentation of the MR images of the Multiple Sclerosis (MS). The Magnetic Resonance Imaging (MRI) allows the visualization of the brain and it is widely used in the diagnosis and the follow-up of the patients suffering from MS. Aiming to automate a long and tedious process for the clinician, we propose the automatic segmentation of the MS lesions. Our algorithm of segmentation is composed of three stages: segmentation of the brain into regions using the algorithm Fuzzy Particle Swarm Optimization (FPSO) in order to obtain the characterization of the different healthy tissues (White matter, grey matter and cerebrospinal fluid (CSF)) after the extraction of white matter (WM), the elimination of the atypical data (outliers) of the white matter by the algorithm Fuzzy C-Means (FCM), finally, the use of a Mamdani-type fuzzy model to extract the MS lesions among all the absurd data.

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Series: Journal of medical imaging and health informatics
ISSN: 2156-7018
ISSN-E: 2156-7026
ISSN-L: 2156-7018
Volume: 9
Issue: 6
Pages: 1119 - 1130
DOI: 10.1166/jmihi.2019.2690
Type of Publication: A1 Journal article – refereed
Field of Science: 114 Physical sciences
Copyright information: © 2019 American Scientific Publishers. Self-archived with the kind permission of the publisher.