Computer-Aided Diagnosis System for Alzheimer's Disease Using Fuzzy-Possibilistic Tissue Segmentation and SVM Classification

Lilia Lazli1, Mounir Boukadoum, Otmane Ait Mohamed2

  • 1Université du Québec à Montréal
  • 2Concordia University



Poster Session


10:00 - 17:00 | Mon 29 Oct | Foyer | A1P-C

Bio-Circuits & Systems

Full Text


We describe a computer-aided diagnosis (CAD) system for discriminating patients suffering from Alzheimer’s disease (AD) and healthy patients. It is based on: 1) a clustering process to assess white matter, gray matter and cerebrospinal fluid volumes from noisy anatomical magnetic resonance (MR) and functional positron emission tomography (fPET) brain images; 2) a classification process that distinguishes the brain images of normal and AD patients. The clustering stage consists of three steps: First, the fuzzy c-mean (FCM) algorithm is used to provide a fuzzy partition of the initial centroids. Second, fuzzy tissue maps are computed using a possibilistic c-means (PCM) algorithm that uses the FCM partition to obtain the final image clusters. The final segmentation is then made to delimit the brain tissue volumes. For the classification stage, a support vector machine (SVM) is used with different kernel functions. Our experimental results show that our system yields higher sensitivity and specificity rates than alternative approaches.

Additional Information

No information added


No videos found