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Biomarker-Based Signature of Alzheimer’s Disease in Pre-MCI Individuals
Open AccessArticle

Computer-Aided Diagnosis System of Alzheimer’s Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model

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Department of Electrical Engineering, École de Technologie Supérieure, ÉTS, University of Quebec, Montreal, QC H3C 1K3, Canada
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CoFaMic Research Center, Computer Science Department, Université du Québec à Montréal, UQAM, University of Quebec, Montreal, QC H3C 3P8, Canada
3
Computer Science Department, Faculty of Engineering Sciences, University of Badji Mokhtar Annaba, UBMA, Annaba 23000, Algeria
4
Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(10), 289; https://doi.org/10.3390/brainsci9100289
Received: 9 October 2019 / Accepted: 17 October 2019 / Published: 22 October 2019
(This article belongs to the Special Issue Biomarkers for Early Diagnosis of Dementia)
An improved computer-aided diagnosis (CAD) system is proposed for the early diagnosis of Alzheimer’s disease (AD) based on the fusion of anatomical (magnetic resonance imaging (MRI)) and functional (8F-fluorodeoxyglucose positron emission tomography (FDG-PET)) multimodal images, and which helps to address the strong ambiguity or the uncertainty produced in brain images. The merit of this fusion is that it provides anatomical information for the accurate detection of pathological areas characterized in functional imaging by physiological abnormalities. First, quantification of brain tissue volumes is proposed based on a fusion scheme in three successive steps: modeling, fusion and decision. (1) Modeling which consists of three sub-steps: the initialization of the centroids of the tissue clusters by applying the Bias corrected Fuzzy C-Means (FCM) clustering algorithm. Then, the optimization of the initial partition is performed by running genetic algorithms. Finally, the creation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) tissue maps by applying the Possibilistic FCM clustering algorithm. (2) Fusion using a possibilistic operator to merge the maps of the MRI and PET images highlighting redundancies and managing ambiguities. (3) Decision offering more representative anatomo-functional fusion images. Second, a support vector data description (SVDD) classifier is used that must reliably distinguish AD from normal aging and automatically detects outliers. The “divide and conquer” strategy is then used, which speeds up the SVDD process and reduces the load and cost of the calculating. The robustness of the tissue quantification process is proven against noise (20% level), partial volume effects and when inhomogeneities of spatial intensity are high. Thus, the superiority of the SVDD classifier over competing conventional systems is also demonstrated with the adoption of the 10-fold cross-validation approach for synthetic datasets (Alzheimer disease neuroimaging (ADNI) and Open Access Series of Imaging Studies (OASIS)) and real images. The percentage of classification in terms of accuracy, sensitivity, specificity and area under ROC curve was 93.65%, 90.08%, 92.75% and 97.3%; 91.46%, 92%, 91.78% and 96.7%; 85.09%, 86.41%, 84.92% and 94.6% in the case of the ADNI, OASIS and real images respectively. View Full-Text
Keywords: Alzheimer’s disease; CAD system; multimodal fusion; tissue volume quantification; bias corrected FCM clustering; genetic optimization; possibilistic FCM clustering; SVDD classifier Alzheimer’s disease; CAD system; multimodal fusion; tissue volume quantification; bias corrected FCM clustering; genetic optimization; possibilistic FCM clustering; SVDD classifier
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Lazli, L.; Boukadoum, M.; Ait Mohamed, O. Computer-Aided Diagnosis System of Alzheimer’s Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model. Brain Sci. 2019, 9, 289.

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