Early Alzheimer Disease Detection by Bag of Visual Words and Hybrid Fusion on Structural Brain MRI

In this paper, we tackle the problem of recognition of Alzheimer's disease (AD) in structural MRI images using visual similarity. AD yields visible changes in the brain structures. We aim to recognize patient category such as AD, or prodromal stage of the AD called Mild Cognitive impairment (MCI), or normal control subject (NC).We use visual local descriptors and the bag of words approach on the most involved regions in AD (Hippocampus and PosteriorCingulate Cortex ) in MRI images. The Content- Based Visual information retrieval (CBVIR)approach is then applied to recognize patient category. The contribution of the paper is in the fusion of visual signatures and of classification results obtained on characteristic brain regions.

[1]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  J B Poline,et al.  Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease. , 2007, Brain : a journal of neurology.

[3]  Marie Chupin,et al.  Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging , 2009, NeuroImage.

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Javed Mostafa,et al.  Content-based image retrieval for Alzheimer's disease detection , 2011, 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI).

[6]  Nick C Fox,et al.  Tracking atrophy progression in familial Alzheimer's disease: a serial MRI study , 2006, The Lancet Neurology.

[7]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[8]  Yalin Wang,et al.  Disease classification with hippocampal shape invariants , 2009, Hippocampus.

[9]  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 .

[10]  J. Baron,et al.  Relationships between Hippocampal Atrophy, White Matter Disruption, and Gray Matter Hypometabolism in Alzheimer's Disease , 2008, The Journal of Neuroscience.

[11]  Payel Ghosh,et al.  Review of medical image retrieval systems and future directions , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).

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

[13]  Ahmet Ekin,et al.  Automated diagnosis of Alzheimer's disease using image similarity and user feedback , 2009, CIVR '09.

[14]  J R Hodges,et al.  Retrosplenial cortex (BA 29/30) hypometabolism in mild cognitive impairment (prodromal Alzheimer's disease) , 2003, The European journal of neuroscience.

[15]  Josephine Barnes,et al.  Application of automated medial temporal lobe atrophy scale to Alzheimer disease. , 2007, Archives of neurology.

[16]  Alessandro Neri,et al.  Keypoints Selection in the Gauss Laguerre Transformed Domain , 2006, BMVC.

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

[18]  Nick C Fox,et al.  The clinical use of structural MRI in Alzheimer disease , 2010, Nature Reviews Neurology.

[19]  S. Black,et al.  Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD , 2001 .

[20]  Michèle Allard,et al.  Feature-based brain MRI retrieval for Alzheimer disease diagnosis , 2012, 2012 19th IEEE International Conference on Image Processing.

[21]  Michèle Allard,et al.  Distinctive alterations of the cingulum bundle during aging and Alzheimer’s disease , 2010, Neurobiology of Aging.

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

[23]  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.