干旱区生态环境知识资源中心(Arid): 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

Arid

DOI	10.3390/brainsci9100289
	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
	Lazli, Lilia 1,2,3; Boukadoum, Mounir 2; Mohamed, Otmane Ait 4
通讯作者	Lazli, Lilia
来源期刊	BRAIN SCIENCES
EISSN	2076-3425
出版年	2019
卷号	9 期号:10
英文摘要	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 (F-8-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.
英文关键词	Alzheimer's disease CAD system multimodal fusion tissue volume quantification bias corrected FCM clustering genetic optimization possibilistic FCM clustering SVDD classifier
类型	Article
语种	英语
国家	Canada ; Algeria
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000493515400029
WOS关键词	C-MEANS ALGORITHM ; SEGMENTATION ; BRAIN ; MR ; INFORMATION ; IMAGES ; FCM
WOS类目	Neurosciences
WOS研究方向	Neurosciences & Neurology
EI主题词	2019-10-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310161
作者单位	1.Univ Quebec, Dept Elect Engn, ETS, Montreal, PQ H3C 1K3, Canada; 2.Univ Quebec, CoFaMic Res Ctr, Comp Sci Dept, UQAM,Univ Quebec Montreal, Montreal, PQ H3C 3P8, Canada; 3.Univ Badji Mokhtar Annaba, Fac Engn Sci, Comp Sci Dept, Annaba 23000, Algeria; 4.Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 1M8, Canada
推荐引用方式 GB/T 7714	Lazli, Lilia,Boukadoum, Mounir,Mohamed, Otmane Ait. 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[J],2019,9(10).
APA	Lazli, Lilia,Boukadoum, Mounir,&Mohamed, Otmane Ait.(2019).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 SCIENCES,9(10).
MLA	Lazli, Lilia,et al."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 SCIENCES 9.10(2019).