Content-based image retrieval for Alzheimer's disease detection

This paper describes ViewFinder Medicine (vfM) as an application of content-based image retrieval to the domain of Alzheimer's disease and medical imaging in general. The system follows a multi-tier architecture which provides the flexibility in experimenting with different representation, classification, ranking and feedback techniques. Classification is central to the system because besides providing an estimate of what stage of the disease the input query may belong to, it also helps adapt and rank the search results. It was found that using our multi-level approach, the classification performance matched the best result reported in the medical imaging literature. Up to 87% of patients were correctly classified in their respective classes, leading to an average precision of about 0.8 without any relevance feedback from the user. To encourage engagement and leverage physicians' knowledge, a relevance feedback function was subsequently added and as result precision improved to 0.89.

[1]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  George Pengas,et al.  Self administered cognitive screening test (TYM) for detection of Alzheimer’s disease: cross sectional study , 2009, BMJ : British Medical Journal.

[3]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[4]  James Zijun Wang,et al.  Pathfinder: multiresolution region-based searching of pathology images using IRM , 2000, AMIA.

[5]  Janaina Mourão Miranda,et al.  Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data , 2005, NeuroImage.

[6]  A. Kak,et al.  Automated storage and retrieval of thin-section CT images to assist diagnosis: system description and preliminary assessment. , 2003, Radiology.

[7]  Matti Pietikäinen,et al.  Image Analysis with Local Binary Patterns , 2005, SCIA.

[8]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[9]  Matti Pietikäinen,et al.  Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2000, ECCV.

[10]  Pramod K. Singh.  Unsupervised Segmentation of Medical Images using DCT Coefficients , 2003, VIP.

[11]  L. Berg.  Clinical Dementia Rating (CDR). , 1988, Psychopharmacology bulletin.

[12]  Dezhong Yao,et al.  A Support Vector Machine Based Algorithm for Magnetic Resonance Image Segmentation , 2008, 2008 Fourth International Conference on Natural Computation.

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

[14]  Moon-Chuen Lee,et al.  Rotation and Scale Invariant Wavelet Feature for Content-based Texture Image Retrieval , 2003, J. Assoc. Inf. Sci. Technol..

[15]  Chuin-Mu Wang,et al.  Classification for Breast MRI Using Support Vector Machine , 2008, 2008 IEEE 8th International Conference on Computer and Information Technology Workshops.

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

[17]  Nick C Fox,et al.  Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[18]  A. Ercil,et al.  Robustness of Local Binary Patterns in Brain MR Image Analysis , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.