ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Neuroimaging and Pattern Recognition Techniques for Automatic Detection of Alzheimer's Disease: A Review


Affiliations
1 Department of Electronics and Telecommunication Engineering, College of Engineering, Pune, India
2 Department of Electronics and Telecommunication Engineering, P.E.S's Modern College of Engineering, India
     

   Subscribe/Renew Journal


Alzheimer's disease (AD) is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression. A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD. In recent years, Neuroimaging techniques combined with machine learning algorithms have received lot of attention in this field. There is a need for development of automated techniques to detect the disease well before patient suffers from irreversible loss. This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study. This review provides detailed comparison of different Neuroimaging techniques and reveals potential application of machine learning algorithms in medical image analysis; particularly in AD enabling even the early detection of the disease- the class labelled as Multiple Cognitive Impairment.

Keywords

Image Classification, Feature Extraction, Computer Aided Diagnosis, Image Databases, Image Analysis, Alzheimer’s Disease.
Subscription Login to verify subscription
User
Notifications
Font Size

  • P.A. Freeborough and N.C. Fox, “MR Image Texture Analysis Applied to the Diagnosis and Tracking of Alzheimer’s Disease”, IEEE Transactions on Medical Imaging, Vol. 17, No. 3, pp. 475-478, 1998.

  • Kirsi Juottonen, Mikko P. Laakso, Kaarina Partanen and Hilkka Soininen, “Comparative MR Analysis of the Entorhinal Cortex and Hippocampus in Diagnosing Alzheimer Disease” , American Journal of Neuroradiology, Vol. 20, No. 1, pp. 139-144, 1999.

  • M.S. Albert, M.B. Moss, R. Tanzi and K. Jones, “Preclinical Prediction of AD using Neuropsychological Tests” , Journal of the International Neuropsychological Society, Vol. 7, No. 5, pp. 631-639, 2001.

  • D.P. Devanand, X. Liu, A. Khandji, S.D. Santi, S. Segal, H. Rusinek, G.H. Pelton, L.S. Honig, R. Mayeux, Y. Stern, M.H. Tabert and M. J. Leon, “Hippocampal and Entrohinal Atrophy In Mild Cognitive Impairment Prediction of Alzheimer Disease” , Neurology, Vol. 68, No. 11, pp. 828-836, 2007.

  • B. Magnin, L. Mesrob, S. Kinkingnehun, M. P.-Issac, O.Colliot, M. Sarazin, B. Dubois, S. Lehericy, and H. Benali, “Support Vector Machine-based Classification of Alzheimer’s Disease from Whole-Brain Anatomical MRI” , Neuroradiology, Vol. 51, No. 2, pp. 73-83, 2008.

  • Simon Duchesne, Anna Caroli, C. Geroldi, Christian Barillot, Giovanni B. Frisoni and D. Louis Collins, “MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls”, IEEE Transactions on Medical Imaging, Vol. 27, No. 4, pp. 509-520, 2008.

  • E. Gerardin, “Multidimensional Classification of Hippocampal Shape Features Discriminates Alzheimer's Disease and Mild Cognitive Impairment from Normal Aging”, Neuroimage, Vol. 47, No. 4, pp. 1476-1486, 2009.

  • R.S. Desikan et al., “Automated MRI Measures Identify Individuals with Mild Cognitive Impairment and Alzheimer’s Disease”, American Journal of Neuroradiology, Vol. 132, No. 8, pp. 2048-2057, 2009.

  • J.H. Morra, Z. Tu, L.G. Apostolova, A.E. Green, A.W. Toga and P.M. Thompson, “Comparison of Ada Boost and Support Vector Machines for Detecting Alzheimer’s Disease through Automated Hippocampal Segmentation”, IEEE Transactions on Medical Imaging, Vol. 29, No. 1, pp. 30-43, 2010.

  • K.B. Walhovd et.al., “Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease” , American Journal of Neuroradiology, Vol. 31, No. 2, pp. 347-354, 2010.

  • D.H. Ye, K.M. Pohl and C. Davatzikos, “Semi-Supervised Pattern Classification: Application to Structural MRI of Alzheimer’s Disease”, Proceedings of IEEE International Workshop on Pattern Recognition in Neuroimaging, pp. 14, 2011.

  • Y. Fan, “Ordinal Ranking for Detecting Mild Cognitive Impairment and Alzheimer’s Disease based on Multimodal Neuroimages and CSF Biomarkers” , Proceedings of International Workshop on Multimodal Brain Image Analysis, pp. 44-51, 2011.

  • J.E. Iglesias, J. Jiang, C. Liu and Z. Tu, “Classification of Alzheimer’s Disease using a Self-Smoothing Operator” , Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 58-65, 2011.

  • D. Zhanga, Y. Wanga, L. Zhoua, H. Yuana and D. Shen, “Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment”, Neuroimage, Vol. 55, No. 3, pp. 856-867, 2011.

  • R. Umer, “Machine Learning Approaches for the Computer Aided Diagnosis and Prediction of Alzheimer’s Disease Based on Clinical Data”, PhD Dissertation, Department of Computer Science, University of Georgia, 2011.

  • A.B. Tufail, A. Abidi, A.M. Siddiqui and M.S. Younis, “Automatic Classification of Initial Categories of Alzheimer’s Disease from Structural MRI Phase Images: A Comparison of PSVM, KNN and ANN Methods”, World Academy of Science, Engineering and Technology, Vol. 6, No. 12, pp. 1570-1574, 2012.

  • R. Casanova, F.C. Hsu and M.A. Espeland, “Classification of Structural MRI Images in Alzheimer’s Disease from the Perspective of III-Posed Problems”, Worldwide Alzheimer's Disease Neuroimaging Initiative, Vol. 7, No. 10, pp. 1-6, 2012.

  • Jayapathy Rajeesh, Rama Swamy Moni and Thankappan Gopalakrishnan, “Discrimination of Alzheimer’s Disease using Hippocampus Texture Features from MRI” , Asian Biomedicine, Vol. 6, No. 1, pp. 87-94, 2012.

  • M. Liu, D. Zhang, P.T. Yap and D. Shen, “Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer's disease”, Proceedings of International Workshop on Machine Learning in Medical Imaging, pp. 2735, 2012.

  • I.V. Maksimovich, “Certain New Aspects of Etiology and Pathogenesis of Alzheimer’s Disease,” Advances in Alzheimer’s Disease, Vol. 1, No.3, pp. 68-76, 2012.

  • K.S. Marcolini and S. Gillespie, “Comparing Classification Methods of MRI Brain Scans for Dementia and Alzheimer's Disease”, Master Thesis, University of Miami, 2012.

  • D. Guan, W. Yuan, “A Survey on Mislabled Training Data Detection Techniques for Pattern Classification” , IETE Technical Review, Vol. 30, No. 6, pp. 524-530, 2013.

  • G.W. Jijl and M. Rangini, “Detection of Alzheimer's Disease through Automated Hippocampal Segmentation” , Proceedings of International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing, pp. 144-149, 2013.

  • E. Westman, C. Aguilar, J. Muehlboeck and A. Simmons, “Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer’s Disease and Mild Cognitive Impairment”, Brain Topography, Vol. 26, No. 1, pp. 9-23, 2013.

  • N. Hu, J. Tai Yu, L. Tan, Y. Wang, L. Sun and L. Tan, “Nutrition and the Risk of Alzheimer’s Disease” , BioMed Research International, Vol. 2013, pp. 1-12, 2013.

  • X. Yan, “Advances in Alzheimer’s Disease (AAD): Standing Firm at its First Anniversary”, Advances in Alzheimer’s Disease, Vol. 2, No. 2, pp. 49-50, 2013.

  • S. Farhan, M. A. Fahiem, F. Tahir, and H. Tauseef, “A Comparative Study of Neuroimaging and Pattern Recognition Techniques for Estimation of Alzheimer’s Disease” , Life Science Journal, Vol. 10, No. 7, pp. 10301039, 2013.

  • R.L. Simoes, “Towards Earlier Detection of Alzheimer’s Disease using Magnetic Resonance Images” , PhD Dissertation, University of Twente, 2013.

  • A. Norouzi, M.S.M. Rahim, A. Altameem, T. Saba, Abdolvahab E. Rad, Amjad Rehman and M. Uddin, “Medical Image Segmentation Methods, Algorithms, and Application”, IETE Technical Review, Vol. 31, No. 3, pp. 199-213, 2014.

  • A. Rueda, F.A. Gonzalez and E. Romero, “Extracting Salient Brain Patterns for Imaging-Based Classification of Neurodegenerative Diseases”, IEEE Transactions on Medical Imaging, Vol. 33, No. 6, pp. 1262-1274, 2014.

  • Q. Zhou, M. Goryawala, M. Cabrerizo, J. Wang, W. Barker, D.A. Loewenstein, R. Duara and M. Adjouadi, “An Optimal Decisional Space for the Classification of Alzheimer’s Disease and Mild Cognitive Impairment” , IEEE Transactions on Biomedical Engineering, Vol. 61, No. 8, pp. 2245-2253, 2014.

  • S. Yazdani, R. Yusuf, A. Karimian, M. Pasha and A. Hematian, “Image Segmentation Methods and Applications in MRI Brain Images” , IETE Technical Review, Vol. 32, No. 6, pp. 413-427, 2015.

  • J. Dauwels, F.B. Vialatte and A. Cichocki, “On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review”, Advances in Cognitive Neurodynamics, Vol. 2, pp. 709-716, 2010.

  • Alzheimer’s Society, Available at: http://www.alzheimers.org.uk.

  • OASIS Brain Database, Available at: http://www.oasisbrain.org, Accessed on 2012.

  • ADNI Database. Available at: http://adni.loni.usc.edu/ [37] Robert M. Haralick, K. Shanmugam and Its Hak Dinstein, “Textural Features for Image Classification” , IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 6, pp. 610-621, 1973.

  • Wenlu Yang; Halei Xia, Bin Xia, Lok Ming Lui and Xudong Huang, “ICA-based Feature Extraction and Automatic Classification of AD-Related MRI Data”, Proceedings of 6th International Conference on Natural Computation, pp. 1261-1265, 2010.

  • S.E. Fashtakeh, “Early Detection of Alzheimer’s Disease using Structural MRI: A Research Idea”, Life Science Journal, Vol. 9, No. 3, pp. 1072-1079, 2012.

  • Chaturaphat Tanchi, Nipon Theera-Umpon and Sansanee Auephanwiriyakul, “Fully Automatic Brain Segmentation for Alzheimer’s”, Proceedings of 6th International Conference on Soft Computing and Intelligent Systems, pp.1393-1396, 2012.

  • S.T. Yang, “Discrimination between Alzheimer’s disease and Mild Cognitive Impairment using SOM and PSO-SVM” , Computational and Mathematical Methods in Medicine, Vol. 2013, pp. 1-10, 2013.

  • B. Al-Naami, N. Gharaibeh and A. Kheshman, “Automated Detection of Alzheimer’s Disease using Region Growing Technique and Artificial neural Network” , World Academy of Science Engineering and Technology, Vol. 7, No. 5, pp. 204-208, 2013.

  • Y. Zhang, S. Wang and Z. Dong, “Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree”, Progress In Electromagnetic Research, Vol. 144, pp. 171184, 2014.

  • R. Kohavi and G.H. John, “Wrappers for Feature Subset Selection”, Artificial Intelligence, Vol. 97, No. 1-2, pp. 273324, 1997.

  • A. Jain and D. Zongker, “Feature Selection: Evaluation, Application, and Small Sample Performance”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 2, pp. 153-158, 1997.


Abstract Views: 341

PDF Views: 5




  • Neuroimaging and Pattern Recognition Techniques for Automatic Detection of Alzheimer's Disease: A Review

Abstract Views: 341  |  PDF Views: 5

Authors

Rupali Kamathe
Department of Electronics and Telecommunication Engineering, College of Engineering, Pune, India
Kalyani Joshi
Department of Electronics and Telecommunication Engineering, P.E.S's Modern College of Engineering, India

Abstract


Alzheimer's disease (AD) is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression. A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD. In recent years, Neuroimaging techniques combined with machine learning algorithms have received lot of attention in this field. There is a need for development of automated techniques to detect the disease well before patient suffers from irreversible loss. This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study. This review provides detailed comparison of different Neuroimaging techniques and reveals potential application of machine learning algorithms in medical image analysis; particularly in AD enabling even the early detection of the disease- the class labelled as Multiple Cognitive Impairment.

Keywords


Image Classification, Feature Extraction, Computer Aided Diagnosis, Image Databases, Image Analysis, Alzheimer’s Disease.

References