Abstract
In the present article, we propose a new approach for the segmentation of the MR images of the Multiple Sclerosis (MS) which is an autoimmune inflammatory disease affecting the central nervous system. Our algorithm of segmentation is composed of three stages: segmentation of the brain into regions using the algorithm FCM (Fuzzy C-Means) in order to obtain the characterization of the different healthy tissues (White matter, grey matter and cerebrospinal fluid (CSF)), the elimination of the atypical data (outliers) of the white matter by the optimization algorithm PSOBC (Particle Swarm Optimization-Based image Clustering), finally, the use of a Mamdani-type fuzzy model to extract the MS lesions among all the absurd data.
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Zouaoui, H., Moussaoui, A., Taleb-Ahmed, A., Oussalah, M. (2016). A New Optimal Neuro-Fuzzy Inference System for MR Image Classification and Multiple Scleroses Detection. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_45
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