A Robust Method for MR Image Segmentation and Multiple Scleroses Detection

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.

Zouaoui H., Moussaoui A., Oussalah M., Taleb-Ahmed A.

Publication type:
A1 Journal article – refereed

Place of publication:

Fuzzy C-Means, Fuzzy Controller, Magnetic Resonance Imaging, Multiple Sclerosis, Particle Swarm Optimization, Segmentation


Full citation:
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. https://doi.org/10.1166/jmihi.2019.2690


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