Predictive Approach of Multiple Sclerosis MR-Lesions Evolution based on Chaotic Attributes

The diagnosis, evaluation, and treatment of multiple sclerosis are based essentially on a visual analysis of brain MR-Images. The main objective of this paper is to propose a new predictive approach of the multiple sclerosis lesions evolution's in order to computerise the daily routine and sometimes difficult diagnostic process and moreover the follow-up. Hence, we chose to introduce the chaos theory. This theory can be used to provide a better understanding of complex systems whose comportment is unpredictable in the long term due to their high sensitivity to initial conditions. A quantitative study is presented in this paper to validate our results using the BrainWeb simulator, MICCAI2008 and MICCAI2016. This funded research was tested on 10 clinical cases (5 pathological patients and 5 healthy cases). Patients with multiple sclerosis are divided into 2 men and 3 women with an age group ranging from 30 to 72 years. The proposed method presents promising results showing the robustness of our segmentation as well as insights into multiple sclerosis which can act as a guideline for the medical neurology researchers. It presents a novel method to analyze the MR-images using the chaotic theory.