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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  1/2018 - 8

An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features

MALIK, F. See more information about MALIK, F. on SCOPUS See more information about MALIK, F. on IEEExplore See more information about MALIK, F. on Web of Science, FARHAN, S. See more information about  FARHAN, S. on SCOPUS See more information about  FARHAN, S. on SCOPUS See more information about FARHAN, S. on Web of Science, FAHIEM, M. A. See more information about FAHIEM, M. A. on SCOPUS See more information about FAHIEM, M. A. on SCOPUS See more information about FAHIEM, M. A. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,228 KB) | Citation | Downloads: 149 | Views: 571

Author keywords
biomedical image processing, computer aided diagnosis, feature extraction, image classification, pattern recognition

References keywords
alzheimer(51), disease(38), imaging(12), functional(12), fmri(12), brain(11), diagnosis(10), dementia(8), classification(8), neuroimage(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01008
SCOPUS ID: 85043280771

Abstract
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Alzheimer's is a neurodegenerative disease caused by the destruction and death of brain neurons resulting in memory loss, impaired thinking ability, and in certain behavioral changes. Alzheimer disease is a major cause of dementia and eventually death all around the world. Early diagnosis of the disease is crucial which can help the victims to maintain their level of independence for comparatively longer time and live a best life possible. For early detection of Alzheimer's disease, we are proposing a novel approach based on fusion of multiple types of features including hemodynamic, volumetric and textural features of the brain. Our approach uses non-invasive fMRI with ensemble of classifiers, for the classification of the normal controls and the Alzheimer patients. For performance evaluation, ten-fold cross validation is used. Individual feature sets and fusion of features have been investigated with ensemble classifiers for successful classification of Alzheimer's patients from normal controls. It is observed that fusion of features resulted in improved results for accuracy, specificity and sensitivity.


References | Cited By  «-- Click to see who has cited this paper

[1] A. S. Association, "2016 Alzheimer's disease facts and figures," Alzheimer's & Dementia, vol. 12(4), pp. 459-509, 2016.
[CrossRef] [SCOPUS Times Cited 389]


[2] A. Golby, G. Silverberg, E. Race, S. Gabrieli, J. O'Shea, et al., "Memory encoding in Alzheimer's disease: an fMRI study of explicit and implicit memory," Brain, vol. 128, pp. 773-787, 2005.
[CrossRef] [Web of Science Times Cited 135] [SCOPUS Times Cited 154]


[3] M. S. Albert, S. T. DeKosky, D. Dickson, B. Dubois, H. H. Feldman, et al., "The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease," Alzheimer's & dementia, vol. 7, pp. 270-279, 2011.
[CrossRef] [Web of Science Times Cited 2634] [SCOPUS Times Cited 2925]


[4] C. R. Jack, M. S. Albert, D. S. Knopman, G. M. McKhann, R. A. Sperling, et al., "Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease," Alzheimer's & Dementia, vol. 7, pp. 257-262, 2011.
[CrossRef] [Web of Science Times Cited 597] [SCOPUS Times Cited 700]


[5] G. M. McKhann, D. S. Knopman, H. Chertkow, B. T. Hyman, C. R. Jack, et al., "The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease," Alzheimer's & dementia, vol. 7, pp. 263-269, 2011.
[CrossRef] [Web of Science Times Cited 3467] [SCOPUS Times Cited 3860]


[6] R. A. Sperling, P. S. Aisen, L. A. Beckett, D. A. Bennett, S. Craft, et al., "Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease," Alzheimer's & dementia, vol. 7, pp. 280-292, 2011.
[CrossRef] [Web of Science Times Cited 2209] [SCOPUS Times Cited 2458]


[7] S. G. Mueller, M. W. Weiner, L. J. Thal, R. C. Petersen, C. R. Jack, et al., "Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI)," Alzheimer's & Dementia, vol. 1, pp. 55-66, 2005.
[CrossRef] [SCOPUS Times Cited 445]


[8] K. A. Johnson, N. C. Fox, R. A. Sperling, and W. E. Klunk, "Brain imaging in Alzheimer disease," Cold Spring Harbor perspectives in medicine, vol. 2, p. a006213, 2012.
[CrossRef] [Web of Science Times Cited 129] [SCOPUS Times Cited 164]


[9] L. Mosconi, M. Brys, L. Glodzik-Sobanska, S. De Santi, H. Rusinek, et al., "Early detection of Alzheimer's disease using neuroimaging," Experimental gerontology, vol. 42, pp. 129-138, 2007.
[CrossRef] [Web of Science Times Cited 86] [SCOPUS Times Cited 104]


[10] J. R. Petrella, R. E. Coleman, and P. M. Doraiswamy, "Neuroimaging and Early Diagnosis of Alzheimer Disease: A Look to the Future 1," Radiology, vol. 226, pp. 315-336, 2003.
[CrossRef] [Web of Science Times Cited 209] [SCOPUS Times Cited 261]


[11] S. Farhan, M. A. Fahiem, and H. Tauseef, "An ensemble-of-classifiers based approach for early diagnosis of Alzheimer's disease: classification using structural features of brain images," Computational and mathematical methods in medicine, vol. 2014, 2014.

[12] M. D'Esposito, E. Zarahn, G. K. Aguirre, and B. Rypma, "The effect of normal aging on the coupling of neural activity to the bold hemodynamic response," Neuroimage, vol. 10, pp. 6-14, 1999.
[CrossRef] [Web of Science Times Cited 316] [SCOPUS Times Cited 347]


[13] R. L. Buckner, A. Z. Snyder, A. L. Sanders, M. E. Raichle, and J. C. Morris, "Functional brain imaging of young, nondemented, and demented older adults," 2006

[14] S. M. Smith, P. M. Matthews, and P. Jezzard, Functional MRI: an introduction to methods: Oxford university press, 2001

[15] S. S. Bassett, D. M. Yousem, C. Cristinzio, I. Kusevic, M. A. Yassa, et al., "Familial risk for Alzheimer's disease alters fMRI activation patterns," Brain, vol. 129, pp. 1229-1239, 2006.
[CrossRef] [Web of Science Times Cited 108] [SCOPUS Times Cited 115]


[16] S. Y. Bookheimer, M. H. Strojwas, M. S. Cohen, A. M. Saunders, M. A. Pericak-Vance, et al., "Patterns of brain activation in people at risk for Alzheimer's disease," New England journal of medicine, vol. 343, pp. 450-456, 2000.
[CrossRef] [Web of Science Times Cited 830] [SCOPUS Times Cited 920]


[17] K. R. Thulborn, C. Martin, and J. T. Voyvodic, "Functional MR imaging using a visually guided saccade paradigm for comparing activation patterns in patients with probable Alzheimer's disease and in cognitively able elderly volunteers," American Journal of Neuroradiology, vol. 21, pp. 524-531, 2000.

[18] R. L. Buckner, "Event-related fMRI and the hemodynamic response," Human brain mapping, vol. 6, pp. 373-377, 1998.
[CrossRef] [SCOPUS Times Cited 209]


[19] A. C. Burggren and S. Y. Bookheimer, "Structural and functional neuroimaging in Alzheimer's disease: an update," Current topics in medicinal chemistry, vol. 2, pp. 385-393, 2002.
[CrossRef] [SCOPUS Times Cited 19]


[20] S.-J. Li, Z. Li, G. Wu, M.-J. Zhang, M. Franczak, et al., "Alzheimer disease: evaluation of a functional mr imaging index as a marker 1," Radiology, vol. 225, pp. 253-259, 2002.
[CrossRef] [Web of Science Times Cited 197] [SCOPUS Times Cited 217]


[21] G. Grön, D. Bittner, B. Schmitz, A. P. Wunderlich, and M. W. Riepe, "Subjective memory complaints: objective neural markers in patients with Alzheimer's disease and major depressive disorder," Annals of neurology, vol. 51, pp. 491-498, 2002.
[CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 103]


[22] M. Grossman, P. Koenig, C. DeVita, G. Glosser, P. Moore, et al., "Neural basis for verb processing in Alzheimer's disease: an fMRI study," Neuropsychology, vol. 17, p. 658, 2003.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 35]


[23] C. Lustig, A. Z. Snyder, M. Bhakta, K. C. O'Brien, M. McAvoy, et al., "Functional deactivations: change with age and dementia of the Alzheimer type," Proceedings of the National Academy of Sciences, vol. 100, pp. 14504-14509, 2003.
[CrossRef] [Web of Science Times Cited 461] [SCOPUS Times Cited 504]


[24] R. Sperling, J. Bates, E. Chua, A. Cocchiarella, D. Rentz, et al., "fMRI studies of associative encoding in young and elderly controls and mild Alzheimer's disease," Journal of Neurology, Neurosurgery & Psychiatry, vol. 74, pp. 44-50, 2003.
[CrossRef] [Web of Science Times Cited 278] [SCOPUS Times Cited 314]


[25] M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, "Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI," Proceedings of the National Academy of Sciences of the United States of America, vol. 101, pp. 4637-4642, 2004.
[CrossRef] [Web of Science Times Cited 1901] [SCOPUS Times Cited 2112]


[26] J. R. Petrella, L. Wang, S. Krishnan, M. J. Slavin, S. E. Prince, et al., "Cortical Deactivation in Mild Cognitive Impairment: High-Field-Strength Functional MR Imaging 1," Radiology, vol. 245, pp. 224-235, 2007.
[CrossRef] [Web of Science Times Cited 104] [SCOPUS Times Cited 112]


[27] K. Supekar, V. Menon, D. Rubin, M. Musen, and M. D. Greicius, "Network analysis of intrinsic functional brain connectivity in Alzheimer's disease," PLoS Comput Biol, vol. 4, p. e1000100, 2008.
[CrossRef] [Web of Science Times Cited 511] [SCOPUS Times Cited 612]


[28] M. Liu, D. Zhang, D. Shen, and A. s. D. N. Initiative, "Ensemble sparse classification of Alzheimer's disease," NeuroImage, vol. 60, pp. 1106-1116, 2012.
[CrossRef] [Web of Science Times Cited 115] [SCOPUS Times Cited 135]


[29] N. Belmokhtar and N. Benamrane, "Classification of Alzheimer's disease from 3D structural MRI data," Age, vol. 78, pp. 69-96, 2012.

[30] E. Dinesh, M. S. Kumar, M. Vigneshwar, and T. Mohanraj, "Instinctive classification of Alzheimer's disease using FMRI, pet and SPECT images," in Intelligent Systems and Control (ISCO), 2013 7th International Conference on, 2013, pp. 405-409.
[CrossRef] [SCOPUS Times Cited 6]


[31] S. Farhan, M. A. Fahiem, F. Tahir, and H. Tauseef, "A Comparative Study of Neuroimaging and Pattern Recognition Techniques for Estimation of Alzheimer's," Life Science Journal, vol. 10, 2013.

[32] J. Ashburner, G. Barnes, C. Chen, J. Daunizeau, G. Flandin, et al., "SPM8 manual," Functional Imaging Laboratory, Institute of Neurology, 2008, 41, pp. 25-58.

[33] A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, et al., "Automated multi-modality image registration based on information theory," in Information processing in medical imaging, 1995, pp. 263-274.

[34] E. E. Tripoliti, D. I. Fotiadis, and M. Argyropoulou, "A supervised method to assist the diagnosis of Alzheimer's disease based on functional magnetic resonance imaging," in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 3426-3429.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 11]


[35] E. E. Tripoliti, D. I. Fotiadis, M. Argyropoulou, and G. Manis, "A six stage approach for the diagnosis of the Alzheimer's disease based on fMRI data," Journal of biomedical informatics, vol. 43, pp. 307-320, 2010.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 42]


[36] S. Sandor and R. Leahy, "Surface-based labeling of cortical anatomy using a deformable atlas," IEEE transactions on medical imaging, vol. 16, pp. 41-54, 1997.
[CrossRef] [Web of Science Times Cited 180] [SCOPUS Times Cited 198]


[37] D. W. Shattuck, S. R. Sandor-Leahy, K. A. Schaper, D. A. Rottenberg, and R. M. Leahy, "Magnetic resonance image tissue classification using a partial volume model," NeuroImage, vol. 13, pp. 856-876, 2001.
[CrossRef] [Web of Science Times Cited 577] [SCOPUS Times Cited 649]


[38] 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," in Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on, 2009, pp. 437-440.
[CrossRef] [SCOPUS Times Cited 3]


[39] D. Schmitter, A. Roche, B. Maréchal, D. Ribes, A. Abdulkadir, et al., "An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease," NeuroImage: Clinical, vol. 7, pp. 7-17, 2015.
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 39]


[40] S.-T. Yang, J.-D. Lee, C.-H. Huang, J.-J. Wang, W.-C. Hsu, et al., "Computer-Aided Diagnosis of Alzheimer's Disease Using Multiple Features with Artificial Neural Network," in PRICAI 2010: Trends in Artificial Intelligence: 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30–September 2, 2010. Proceedings, B.-T. Zhang and M. A. Orgun, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 699-705.
[CrossRef] [SCOPUS Times Cited 6]


[41] W. H. Nailon, "Texture analysis methods for medical image characterisation. In Biomedical imaging, InTech, 2010.
[CrossRef]


[43] A. M. Hansen, D. Jeske, and W. Kirsch, "A chi-square goodness-of-fit test for autoregressive logistic regression models with applications to patient screening," Journal of biopharmaceutical statistics, vol. 25, pp. 89-108, 2015.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[44] L. Rokach, "Ensemble-based classifiers," Artificial Intelligence Review, vol. 33, pp. 1-39, 2010.
[CrossRef] [Web of Science Times Cited 540] [SCOPUS Times Cited 683]


[45] T. G. Dietterich, "Ensemble methods in machine learning," in International workshop on multiple classifier systems, 2000, pp. 1-15.
[CrossRef]


[46] S. Sarraf and G. Tofighi, "Deep Learning-based Pipeline to Recognize Alzheimer' s Disease using fMRI Data," bioRxiv, p. 066910, 2016.

[47] A. Khazaee, A. Ebrahimzadeh, A. Babajani-Feremi, and A. s. D. N. Initiative, "Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI," Behavioural Brain Research, 2016.

[48] E. Challis, P. Hurley, L. Serra, M. Bozzali, S. Oliver, et al., "Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI," NeuroImage, vol. 112, pp. 232-243, 2015.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 39]


[49] R. Sampath and A. Saradha, "Alzheimer," Research Journal of Applied Sciences, Engineering and Technology, vol. 10, pp. 29-34, 2015.
[CrossRef] [SCOPUS Times Cited 1]


[50] A. Khazaee, A. Ebrahimzadeh, and A. Babajani-Feremi, "Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory," in Biomedical Engineering (ICBME), 2014 21th Iranian Conference on, 2014, pp. 252-257.
[CrossRef] [SCOPUS Times Cited 3]


[51] C.-Y. Wee, P.-T. Yap, D. Zhang, K. Denny, J. N. Browndyke, et al., "Identification of MCI individuals using structural and functional connectivity networks," Neuroimage, vol. 59, pp. 2045-2056, 2012.
[CrossRef] [Web of Science Times Cited 135] [SCOPUS Times Cited 160]


[52] Z. Dai, C. Yan, Z. Wang, J. Wang, M. Xia, et al., "Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3)," Neuroimage, vol. 59, pp. 2187-2195, 2012.
[CrossRef] [Web of Science Times Cited 110] [SCOPUS Times Cited 122]


[53] E. E. Tripoliti, D. I. Fotiadis, and M. Argyropoulou, "An automated supervised method for the diagnosis of Alzheimer's disease based on fMRI data using weighted voting schemes," in International Workshop on Imaging Systems and Techniques, 2008.
[CrossRef] [SCOPUS Times Cited 10]




References Weight

Web of Science® Citations for all references: 16,060 TCR
SCOPUS® Citations for all references: 19,188 TCR

Web of Science® Average Citations per reference: 303 ACR
SCOPUS® Average Citations per reference: 362 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-06-21 13:01 in 300 seconds.




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