Features-based approach for Alzheimer's disease diagnosis using visual pattern of water diffusion in tensor diffusion imaging

In this paper, we propose a feature-based classification framework for Alzheimer's disease (AD) recognition using Tensor Diffusion Imaging (DTI). The main contribution consists in considering the visual pattern of water molecules diffusion in the most involved region in AD (hippocampal area). We use the Circular Harmonic Functions (CHFs) and the Bag-of-Visual-Words approach to build an AD related-signature. The experiments were accomplished first with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and then with the DTI scans of a French epidemiological study: ”Bordeaux-3City”. Experimental results demonstrate that our features-based method applied on the MD maps is able to capture the AD-related atrophy and then classify between AD subjects.

[1]  Pieter Jelle Visser,et al.  New MRI markers for Alzheimer's disease: a meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements , 2011, Alzheimer's & Dementia.

[2]  Fabio A. González,et al.  Bag of Features for Automatic Classification of Alzheimer's Disease in Magnetic Resonance Images , 2012, CIARP.

[3]  Lawrence J. Mazlack,et al.  Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach , 2014, Journal of Neuroscience Methods.

[4]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[5]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[6]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[7]  Marie Chupin,et al.  Automatic classi fi cation of patients with Alzheimer ' s disease from structural MRI : A comparison of ten methods using the ADNI database , 2010 .

[8]  Denis Le Bihan,et al.  Looking into the functional architecture of the brain with diffusion MRI , 2003, Nature Reviews Neuroscience.

[9]  Chokri Ben Amar,et al.  Early Alzheimer disease detection with bag-of-visual-words and hybrid fusion on structural MRI , 2013, 2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI).

[10]  Mohammad Reza Daliri,et al.  Automated Diagnosis of Alzheimer Disease using the Scale-Invariant Feature Transforms in Magnetic Resonance Images , 2012, Journal of Medical Systems.

[11]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[12]  Yong He,et al.  Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3) , 2012, NeuroImage.

[13]  Javed Mostafa,et al.  Image Retrieval for Alzheimer's Disease Detection , 2009, MCBR-CDS.

[14]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[15]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

[16]  Chokri Ben Amar,et al.  Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features , 2014, Multimedia Tools and Applications.

[17]  Andrey S. Krylov,et al.  Gauss-Laguerre Keypoints Extraction Using Fast Hermite Projection Method , 2011, ICIAR.

[18]  Bixente Dilharreguy,et al.  Structural hippocampal network alterations during healthy aging: a multi-modal MRI study , 2013, Front. Aging Neurosci..

[20]  Ahmet Ekin,et al.  Local Structure-Based Region-of-Interest Retrieval in Brain MR Images , 2010, IEEE Transactions on Information Technology in Biomedicine.