Computerized Image Segmentation of Multiple Sclerosis Lesions Using Fuzzy Level Set Model

Abstract

Multiple sclerosis is an inflammatory autoimmune disease that affects the central nervous system. We can consider that the Magnetic Resonance Imaging is a quantitative assessment and most objective approach for a better understanding of the pathology. Therefore MRI has emerged as a powerful tool for non-invasive diagnosis and description of the natural history of brain pathologies. A semi-automatic segmentation of multiple sclerosis lesions in brain MRI has been widely studied in recent years but in this paper we will be only limit on the automatic segmentation of these plaques disseminated in time and space. We quantitatively validate our results using data augmentation. Having a large dataset is crucial for the performance of our model. However, we can improve the performance of the model by augmenting the data that we already have.