Segmentation of Brain MRI for Detecting Alzheimer's Disease

Background: Alzheimer Disease (AD) represents a major threat to the lives of human beings. In fact, the disease should be detected at an early stage to maximize the chances of survival. Matarial and Methods: Hence, the use of computer means making the diagnostic procedure automatic called: Computer-Assisted Diagnosis (CAD). This procedure is used to assist radiologists in the analysis of the disease; the number of the affected persons continues to grow in recent decades. As to our work, we made a Computer-Assisted Diagnosis for detecting Alzheimer's disease in early step Mild Cognitive Impairment (MCI). Conclusion: Our system contains three parts: Preprocessing, segmentation and a classification step. For the pretreatment step we used the Non-Local Means Filter (NLMF), the deformable model Level Set in the segmentation step to extract the Cortex and Hippocampus. Our contribution is to improve the segmentation step: we determined a priori shape and an automatic position for the initialization. Also, we added a priori knowledge of the surface. For the classification, our method is based on Support Vector Machine (SVM). The proposed system yields 92.5% accuracy in the early diagnosis of the AD.