Recognition of Alzheimer's disease and Mild Cognitive Impairment with multimodal image-derived biomarkers and Multiple Kernel Learning

Autor: Ben Ahmed, Olfa, Benois-Pineau, Jenny, Michelle, Allard, Catheline, Gwenaelle, Ben Amar, Chokri, The Alzheimer'S Disease Neuroimaging Initiative,
Přispěvatelé: Image et Son, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), REsearch Group in Intelligent Machines [Sfax] (REGIM-Lab), École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax (ENIS), co-tutelle, INCIA, Université de Sfax
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Computer science
Cognitive Neuroscience
[SDV]Life Sciences [q-bio]
02 engineering and technology
Disease
Machine learning
computer.software_genre
ACM: G.: Mathematics of Computing
03 medical and health sciences
[SPI]Engineering Sciences [physics]
0302 clinical medicine
Neuroimaging
ACM: H.: Information Systems
Artificial Intelligence
0202 electrical engineering
electronic engineering
information engineering
[INFO]Computer Science [cs]
Multiple kernel learning
business.industry
[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]
Multimodal therapy
Computer Science Applications
Binary classification
MESH: Alzheimer's disease
Multiple Kernel Learning
Mutlimodal fusion
Diagnosis
Local features
DTI
MD maps
CHFs
Imaging biomarkers
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Diffusion MRI
Zdroj: Neurocomputing
Neurocomputing, Elsevier, 2017, Recent Research in Medical Technology Based on Multimedia and Pattern Recognition, 220, pp.98-110. ⟨10.1016/j.neucom.2016.08.041⟩
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2016.08.041⟩
Popis: International audience; Computer-Aided Diagnosis (CAD) of Alzheimer's disease (AD) has drawn the attention of computer vision research community over the last few years. Several attempts have been made to adapt pattern recognition approaches to specific neuroimaging data such as Structural MRI (sMRI) for early AD diagnosis. One strategy is to boost the discrimination power of such approaches by integrating complementary imaging modalities in a single learning framework. Diffusion Tensor Imaging (DTI) is a new and promising modality giving complementary information to the anatomical MRI. However, including relevant DTI information from such modality is a challenging problem. In this paper, we propose to extract local image-derived biomarkers from DTI and sMRI to construct multimodal AD signatures. To assess the relevance of such modalities as well as to optimize the classifier, we integrate complementary information using a Multiple Kernel Learning (MKL) framework for AD subjects recognition. To evaluate our method, we perform experiments on a subset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Both T1-weighted MRI and Mean Diffusivity (MD) maps from the DTI modality of 45 AD patients, 52 Normal Control (NC) and 58 Mild Cognitive Impairment (MCI) subjects have been used. The obtained results indicate that our multimodal approach yields significant improvement in accuracy over using each single modality independently. The classification accuracies obtained by the proposed method are 90.2%, 79.42% and 76.63% for respectively AD vs. NC, MCI vs. NC and AD vs. MCI binary classification problems. For the MCI classification problem, the proposed fusion framework leads to an average increase about at least 9% for the accuracy, 5% for the specificity and 15% for the sensitivity.
Databáze: OpenAIRE
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