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Alzheimer’s Detection at Early Stage Using Modified LGS (M-LGS) on MRI


Affiliations
1 Dept. of Computer Science & Engineering, Adi Shankara College of Engineering & Technology, Ernakulam, Kerala, India
2 Dept. of Information Technology, Higher College of Technology, Muscat, Oman
     

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Alzheimer’s Disease (AD) is the most common cause of dementia, which is a non-curable after a certain stage. The nerve cells, which are very essential to carry messages in the brain, particularly those responsible for storing memories, slowly get damaged due to the formation of tangles and plaques made from protein fragments in damaged areas of the brain. This paper focuses on early detection of Alzheimer’s disease using Magnetic Resonance Imaging (MRI), so that effective medication is possible. The minute changes in the Gray Matter (GM) and white is observed there in the MRI and the processing of this will be an aid to the expert for the correct diagnosis. The GM and WM are segmented from the image and the texture information is extracted using different variants of the Local Binary Pattern (LBP). It is observed that the Modified Local graph structure with the inclusion of grayscale information is a good descriptor for classifying AD and Mild Cognitive Impairment (MCI). The accuracy of the classification is improved by the inclusion of the proper threshold in the formation of local pattern.

Keywords

Alzheimer’s Disease, Local Patterns, Magnetic Resonance Imaging, Mild Cognitive Impairment, Normal Aging, Support Vector Machine.
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  • P. J. Houghton, M. J. Howes, “Natural products and derivatives aecting neurotransmission relevant to Alzheimers and Parkinsons disease,” Neurosignals, vol. 14, pp. 6-22, 2005.

  • M. Greco, “The effects of music theraphy on Alzhiemers,” Brainwaves, pp. 8-10, 2013.

  • T. Manenppa, “The Local binary pattern approach to texture analysis: Extensions and applications,” Thesis, OULU’03, pp. 1-70, 2003.

  • R. A. Sperlind, P. S. Aisen, L. A. Backett, D. A. Bannett, S. Craft, A. M. Fagan,…C. H. Phelps, “Toward defi ning the preclinical stages of Alzheimers disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease,” Alzhimers and Dementia, vol. 7, pp. 280-292, 2011.

  • J. Ren, X. Jiang, and J. Yuan, “Relaxed local ternary pattern for face recognition,” IEEE Conference on Image Processing (ICIP), Sep. 2013.

  • S. Liao, M. Law, and C. S Chung, “Dominant local binary patterns for texture classfication,” IEEE Trans. Image Process., vol. 18, no. 5, pp. 1107-1118, 2009.

  • Z. H. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classfication,” IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1657-1663, 2010.

  • J. Zhang, J. Liang, and H. Zhao, “Local energy pattern for texture classification using self-adaptive quantization thresholds,” IEEE Transactions on Image Processing, vol. 22, no. 1, pp. 31-43, January 2013.

  • E. E. Abusham, H. K. Bashier, F. Khalid, S. Sayeed, J. Hossen, and S. M. A Kalaiarasi, “Illumination normalization using Eimad-Housam technique,” Trends in Applied Sciences and Research, pp. 692-700, 2012.

  • S. Treit, Z. Chen, C. Rasmussen, and C. BEAULIEU, “White matter correlates of cognitive inhibition during development: A diffusion tensor imaging study,” Neuroscience, vol. 16, no. 13, 2013.

  • A. Z. Burzynska, C. Preuschhof, L. Bäckman, L. Nyberg, U. Lindenberger, H. R. Heekeren, “Age-related differences in white matter microstructure: Region-specific patterns of diffusivity,” NeuroImage, vol. 49 , pp. 2104-2112, 2010.

  • J.-D. Lee, S.-C. Su, C.-H. Huang, W.-C. Xu, and Y.-Y. Wei, “Using volume features and shape features for alzheimer’s disease diagnosis,” Fourth International Conference on Innovative Computing, Information and Control, IEEE Computer Society, 2009.

  • M. Sach, and G. Winkler, “Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging,” Brain, pp. 340-350.

  • K. Chung, “Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease,” Neuro Science, 2004.

  • G. B. Frisoni, P. Scheltens, S. Galluzzi, F. M. Nobili, N. C. Fox,…E. Salmon, “Neuroimaging tools to rate regional atrophy, ubcortical cerebrovascular disease, and regional cerebral blood flow and metabolism: Consensus paper of the EADC,” J Neurol Neurosurg Psychiatry, vol. 74, no. 10, pp. 1371-81, Oct. 2003.

  • H. Yang, W. Liu, H, Xia, Z, Zhen, and L. Tong, “Longitudinal change of the grey matter of mild congnitive impairment patients over 3 years by using voxel-based morphometry,” 5th International Conference on BioMedical Engineering and Informatics, IEEE, 2012.

  • T. Varghese, K. R. Sheela, P. S. Mathuranath, S. N. Albert, “Discrimination between Alzheimer’s disease, mild cognitive impairment and normal aging using ANN based MR brain image segmentation,” Advances in Intelligent Systems and Computing, SPRINGER, pp. 129-136, 2014.

  • K. V. Leemput, F. Maes, D. Vandermeulen, and P. Suetens, “Automated model based tissue classification of MR images of brain,” IEEE Transaction on Medical Imaging, pp. 897-908, vol. 18, Oct. 1998.

  • M. F. B. Othman, N. B. Abdullah, and N. F. B. Kamal, “MRI brain classification using support vector machine,” 4th International Conference on Modeling, Simulation and Applied Optimization, Centre for Artificial Intell. & Robot [CAIRO], Kuala Lumpur Malaysia, 2011.

  • R. C. Petersen, P. S. Aisen, L. A. Beckett, M. C. Donohue, A. C. Gamst,…M. W. Weiner, “Alzhiemers Disease Neuroimaging Initiative,” Neurology, vol. 7, pp. 201-209, 2010.


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  • Alzheimer’s Detection at Early Stage Using Modified LGS (M-LGS) on MRI

Abstract Views: 325  |  PDF Views: 3

Authors

S. Nayaki K.
Dept. of Computer Science & Engineering, Adi Shankara College of Engineering & Technology, Ernakulam, Kerala, India
A. B. Varghese
Dept. of Information Technology, Higher College of Technology, Muscat, Oman

Abstract


Alzheimer’s Disease (AD) is the most common cause of dementia, which is a non-curable after a certain stage. The nerve cells, which are very essential to carry messages in the brain, particularly those responsible for storing memories, slowly get damaged due to the formation of tangles and plaques made from protein fragments in damaged areas of the brain. This paper focuses on early detection of Alzheimer’s disease using Magnetic Resonance Imaging (MRI), so that effective medication is possible. The minute changes in the Gray Matter (GM) and white is observed there in the MRI and the processing of this will be an aid to the expert for the correct diagnosis. The GM and WM are segmented from the image and the texture information is extracted using different variants of the Local Binary Pattern (LBP). It is observed that the Modified Local graph structure with the inclusion of grayscale information is a good descriptor for classifying AD and Mild Cognitive Impairment (MCI). The accuracy of the classification is improved by the inclusion of the proper threshold in the formation of local pattern.

Keywords


Alzheimer’s Disease, Local Patterns, Magnetic Resonance Imaging, Mild Cognitive Impairment, Normal Aging, Support Vector Machine.

References