Segmentation of Brain MRI for Detecting Alzheimer's Disease

Author(s): Amira B. Rabeh*, Faouzi Benzarti, Hamid Amiri

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 14 , Issue 2 , 2018

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Graphical Abstract:


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.

Keywords: Alzheimer Disease (AD), Computer-Assisted Diagnostic (CAD), Mild Cognitive Impairment (MCI), Non-local Means Filter (NLMF), Cortex (C), Support Vector Machine (SVM).

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Article Details

Year: 2018
Published on: 16 February, 2018
Page: [263 - 270]
Pages: 8
DOI: 10.2174/1573405613666170116163251
Price: $65

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