Application of FCM Clustering on AM-FM to Detect MRI Disease Progression for Multiple Sclerosis

Particular Regional Atrophy analysis of structural magnetic resonance image (MRI) of the brain may provide quantitative evidence of different neurodegenerative diseases, which will help to identify the Brain diseases. Multiple sclerosis (MS) is one of the most common diseases which affect white matter. Multiple sclerosis is a chronic idiopathic disease resulted in multiple areas of inflammatory demyelization in the Central nervous system. MS lesion formation often leads to unpredictable cognitive decline & Physical disability. This paper proposes an approach for progression detection of the Multiple sclerosis (MS) in the brain along with an automated computer aided system for differential diagnosis of different neurodegenerative diseases by regional atrophy analysis such as the hippocampus that is well known to be affected in early brain disease. Due to the complexity & variance of automated MRI segmentation of brain becomes a complex task. A structural texture analysis method on MRI segmentation scheme gives emphasis on structural analysis of abnormal as well as on normal tissues. AM-FM technique is used for segmentation of the MRI image. Saliency Map detection technique is used to find out the area of interest and important area of the images. In this paper MRI disease progression detection is implemented to predict the condition of the patient. Silency mapping and FCM clustering is used to find out the infected region from the MRI image of the patient. System accuracy has improved due to the introduction of Saliency map and progressive disease detection technique based on count of infected regions.