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

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Poster Session

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

Bio-Circuits & Systems

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Abstract

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.

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