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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  2/2017 - 15

A Novel Approach for the Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's disease using MRI Images

AYUB, A. See more information about AYUB, A. on SCOPUS See more information about AYUB, A. on IEEExplore See more information about AYUB, A. 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, TAUSEEF, H. See more information about TAUSEEF, H. on SCOPUS See more information about TAUSEEF, H. on SCOPUS See more information about TAUSEEF, H. on Web of Science
 
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Download PDF pdficon (1,667 KB) | Citation | Downloads: 109 | Views: 160

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

References keywords
alzheimer(34), disease(29), classification(20), brain(14), neuroimage(13), cognitive(13), structural(12), mild(12), impairment(12), pattern(11)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 113 - 122
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02015
Web of Science Accession Number: 000405378100015
SCOPUS ID: 85020067022

Abstract
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The main objective of our research is to introduce an approach that uses noninvasive MRI images to predict the conversion from mild cognitive impairment to Alzheimer's disease at an early stage. It detects normal controls that are likely to develop Alzheimer's disease and mild cognitive impairment patients that are likely to establish Alzheimer's disease within two years or, contrarily, their stage remains same. The proposed approach uses two types of features i.e. volumetric features and textural features. Volumetric features consist of volume of grey matter, volume of white matter and volume of cerebrospinal fluid. A total of 364 textural features have been calculated. To avoid the curse of dimensionality, textural features are reduced to 15 features using gain ratio, a ranking based search algorithm. All features are tested against four classifiers i.e. AODEsr, VFI, RBF and LBR. Leave-One-Out cross validation strategy is used for the evaluation of proposed approach. Results show accuracy of 98.33% with volumetric features and 100% with textural features using VFI and LBR. Our approach is innovative because of its higher accuracy results as compared to existing approaches yet with a smaller feature set.


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

[1] "2015 Alzheimer's disease facts and figures," Alzheimer's & Dementia, vol. 11, pp. 332-384, 2015.
[CrossRef] [Web of Science Times Cited 305] [SCOPUS Times Cited 483]


[2] R. Brookmeyer, E. Johnson, K. Ziegler-Graham, and H. M. Arrighi, "Forecasting the global burden of Alzheimer's disease," Alzheimer's & dementia, vol. 3, pp. 186-191, 2007
[CrossRef] [Web of Science Times Cited 1264] [SCOPUS Times Cited 1448]


[3] 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

[4] 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," Age, vol. 75, pp. 76.13-7.55, 2012

[5] R. Casanova, F.-C. Hsu, K. M. Sink, S. R. Rapp, J. D. Williamson, et al., "Alzheimer's disease risk assessment using large-scale machine learning methods," PloS one, vol. 8, p. e77949, 2013.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 23]


[6] S. Klöppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, et al., "Automatic classification of MR scans in Alzheimer's disease," Brain, vol. 131, pp. 681-689, 2008.
[CrossRef] [Web of Science Times Cited 508] [SCOPUS Times Cited 590]


[7] Y. Fan, S. M. Resnick, X. Wu, and C. Davatzikos, "Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study," Neuroimage, vol. 41, pp. 277-285, 2008.
[CrossRef] [Web of Science Times Cited 171] [SCOPUS Times Cited 187]


[8] C. Davatzikos, P. Bhatt, L. M. Shaw, K. N. Batmanghelich, and J. Q. Trojanowski, "Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification," Neurobiology of aging, vol. 32, pp. 2322. e19-2322. e27, 2011

[9] 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 99] [SCOPUS Times Cited 125]


[10] K. Kantarci and C. R. Jack, "Neuroimaging in Alzheimer disease: an evidence-based review," Neuroimaging Clinics of North America, vol. 13, pp. 197-209, 2003.
[CrossRef] [Web of Science Times Cited 112]


[11] D. Zhang, Y. Wang, L. Zhou, H. Yuan, D. Shen, et al., "Multimodal classification of Alzheimer's disease and mild cognitive impairment," Neuroimage, vol. 55, pp. 856-867, 2011.
[CrossRef] [Web of Science Times Cited 353] [SCOPUS Times Cited 418]


[12] E. Moradi, A. Pepe, C. Gaser, H. Huttunen, J. Tohka, et al., "Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects," NeuroImage, vol. 104, pp. 398-412, 2015.
[CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 60]


[13] G. Chetelat, B. Desgranges, V. De La Sayette, F. Viader, F. Eustache, et al., "Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment," Neuroreport, vol. 13, pp. 1939-1943, 2002.
[CrossRef]


[14] J. Ashburner and K. J. Friston, "Voxel-based morphometry—the methods," Neuroimage, vol. 11, pp. 805-821, 2000.
[CrossRef] [Web of Science Times Cited 4561] [SCOPUS Times Cited 4889]


[15] J. E. Arco, J. Ramírez, J. M. Gorriz, C. G. Puntonet, and M. Ruz, "Short-term Prediction of MCI to AD conversion based on Longitudinal MRI analysis and neuropsychological tests," in Innovation in Medicine and Healthcare 2015, ed: Springer, 2016, pp. 385-394.
[CrossRef] [Web of Science Record] [SCOPUS Times Cited 2]


[16] C. Davatzikos, S. M. Resnick, X. Wu, P. Parmpi, and C. M. Clark, "Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI," Neuroimage, vol. 41, pp. 1220-1227, 2008.
[CrossRef] [Web of Science Times Cited 124] [SCOPUS Times Cited 137]


[17] R. C. Petersen, "Mild cognitive impairment as a diagnostic entity," Journal of internal medicine, vol. 256, pp. 183-194, 2004.
[CrossRef] [Web of Science Times Cited 2736] [SCOPUS Times Cited 3044]


[18] L. M. Shaw, H. Vanderstichele, M. Knapik-Czajka, C. M. Clark, P. S. Aisen, et al., "Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects," Annals of neurology, vol. 65, pp. 403-413, 2009.
[CrossRef] [Web of Science Times Cited 843] [SCOPUS Times Cited 923]


[19] R. M. Chapman, M. Mapstone, J. W. McCrary, M. N. Gardner, A. Porsteinsson, et al., "Predicting conversion from mild cognitive impairment to Alzheimer's disease using neuropsychological tests and multivariate methods," Journal of clinical and experimental neuropsychology, vol. 33, pp. 187-199, 2011.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 42]


[20] C. R. Jack, V. J. Lowe, M. L. Senjem, S. D. Weigand, B. J. Kemp, et al., "11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment," Brain, vol. 131, pp. 665-680, 2008.
[CrossRef] [Web of Science Times Cited 469] [SCOPUS Times Cited 522]


[21] C. D. Good, R. I. Scahill, N. C. Fox, J. Ashburner, K. J. Friston, et al., "Automatic differentiation of anatomical patterns in the human brain: validation with studies of degenerative dementias," Neuroimage, vol. 17, pp. 29-46, 2002.
[CrossRef] [Web of Science Times Cited 302] [SCOPUS Times Cited 319]


[22] O. Colliot, G. Chételat, M. Chupin, B. Desgranges, B. Magnin, et al., "Discrimination between Alzheimer Disease, Mild Cognitive Impairment, and Normal Aging by Using Automated Segmentation of the Hippocampus 1," Radiology, vol. 248, pp. 194-201, 2008.
[CrossRef] [Web of Science Times Cited 137] [SCOPUS Times Cited 150]


[23] C. Misra, Y. Fan, and C. Davatzikos, "Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI," Neuroimage, vol. 44, pp. 1415-1422, 2009.
[CrossRef] [Web of Science Times Cited 241] [SCOPUS Times Cited 282]


[24] A. Mechelli, C. J. Price, K. J. Friston, and J. Ashburner, "Voxel-based morphometry of the human brain: methods and applications," Current medical Imaging reviews, vol. 1, pp. 105-113, 2005.
[CrossRef] [Web of Science Times Cited 328]


[25] Y. Fan, N. Batmanghelich, C. M. Clark, C. Davatzikos, and A. s. D. N. Initiative, "Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline," Neuroimage, vol. 39, pp. 1731-1743, 2008.
[CrossRef] [Web of Science Times Cited 262] [SCOPUS Times Cited 304]


[26] M. Bozzali, M. Filippi, G. Magnani, M. Cercignani, M. Franceschi, et al., "The contribution of voxel-based morphometry in staging patients with mild cognitive impairment," Neurology, vol. 67, pp. 453-460, 2006.
[CrossRef] [Web of Science Times Cited 118] [SCOPUS Times Cited 133]


[27] G. Chetelat, B. Landeau, F. Eustache, F. Mezenge, F. Viader, et al., "Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study," Neuroimage, vol. 27, pp. 934-946, 2005.
[CrossRef] [Web of Science Times Cited 323] [SCOPUS Times Cited 364]


[28] Y. Hirata, H. Matsuda, K. Nemoto, T. Ohnishi, K. Hirao, et al., "Voxel-based morphometry to discriminate early Alzheimer's disease from controls," Neuroscience letters, vol. 382, pp. 269-274, 2005.
[CrossRef] [Web of Science Times Cited 156] [SCOPUS Times Cited 184]


[29] A. Hämäläinen, S. Tervo, M. Grau-Olivares, E. Niskanen, C. Pennanen, et al., "Voxel-based morphometry to detect brain atrophy in progressive mild cognitive impairment," Neuroimage, vol. 37, pp. 1122-1131, 2007.
[CrossRef] [Web of Science Times Cited 83] [SCOPUS Times Cited 94]


[30] C. Davatzikos, Y. Fan, X. Wu, D. Shen, and S. M. Resnick, "Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging," Neurobiology of aging, vol. 29, pp. 514-523, 2008.
[CrossRef] [Web of Science Times Cited 187] [SCOPUS Times Cited 219]


[31] E. Gerardin, G. Chételat, M. Chupin, R. Cuingnet, B. Desgranges, et al., "Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging," Neuroimage, vol. 47, pp. 1476-1486, 2009.
[CrossRef] [Web of Science Times Cited 165] [SCOPUS Times Cited 187]


[32] M. Chupin, E. Gérardin, R. Cuingnet, C. Boutet, L. Lemieux, et al., "Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI," Hippocampus, vol. 19, pp. 579-587, 2009.
[CrossRef] [Web of Science Times Cited 126] [SCOPUS Times Cited 148]


[33] S. L. Risacher, A. J. Saykin, J. D. Wes, L. Shen, H. A. Firpi, et al., "Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort," Current Alzheimer Research, vol. 6, pp. 347-361, 2009.
[CrossRef] [SCOPUS Times Cited 250]


[34] Y. Fan, D. Shen, and C. Davatzikos, "Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM," in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2005, ed: Springer, 2005, pp. 1-8.
[CrossRef]


[35] A. Farzan, S. Mashohor, A. R. Ramli, and R. Mahmud, "Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns," Behavioural brain research, vol. 290, pp. 124-130, 2015.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 13]


[36] L. Khedher, J. Ramírez, J. Gorriz, A. Brahim, F. Segovia, et al., "Early diagnosis of Alzheimer? s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images," Neurocomputing, vol. 151, pp. 139-150, 2015.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 33]


[37] Y. Zhang, S. Wang, P. Phillips, Z. Dong, G. Ji, et al., "Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC," Biomedical Signal Processing and Control, vol. 21, pp. 58-73, 2015.
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 43]


[38] X. Long and C. Wyatt, "An automatic unsupervised classification of MR images in Alzheimer's disease," in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 2010, pp. 2910-2917.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 12]


[39] 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.

[40] A. Ortiz, J. M. Gorriz, J. Ramírez, F. J. Martínez-Murcia, and A. s. D. N. Initiative, "LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease," Pattern Recognition Letters, vol. 34, pp. 1725-1733, 2013.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 35]


[41] D. H. Ye, K. M. Pohl, and C. Davatzikos, "Semi-supervised pattern classification: application to structural MRI of Alzheimer's disease," in Pattern Recognition in NeuroImaging (PRNI), 2011 International Workshop on, 2011, pp. 1-4.
[CrossRef] [SCOPUS Times Cited 15]


[42] R. Filipovych, C. Davatzikos, and A. s. D. N. Initiative, "Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI)," NeuroImage, vol. 55, pp. 1109-1119, 2011.
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 50]


[43] 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 Electromagnetics Research, vol. 144, pp. 171-184, 2014.
[CrossRef] [Web of Science Times Cited 52]


[44] K. Hu, Y. Wang, K. Chen, L. Hou, and X. Zhang, "Multi-scale features extraction from baseline structure MRI for MCI patient classification and AD early diagnosis," Neurocomputing, vol. 175, pp. 132-145, 2016.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 4]


[45] D. W. Shattuck and R. M. Leahy, "BrainSuite: an automated cortical surface identification tool," Medical image analysis, vol. 6, pp. 129-142, 2002.
[CrossRef]


[46] S. M. Smith and J. M. Brady, "SUSAN—a new approach to low level image processing," International journal of computer vision, vol. 23, pp. 45-78, 1997.
[CrossRef] [Web of Science Times Cited 1360]


[47] 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 545] [SCOPUS Times Cited 617]


[48] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, et al., "The WEKA data mining software: an update," ACM SIGKDD explorations newsletter, vol. 11, pp. 10-18, 2009.

[49] C. Ledig, R. Guerrero, T. Tong, K. Gray, A. Makropoulos, et al., "Alzheimer's disease state classification using structural volumetry, cortical thickness and intensity features," in Proc MICCAI workshop challenge on computer-aided diagnosis of dementia based on structural MRI data, 2014, pp. 55-64.

[50] J. Zhang, C. Yu, G. Jiang, W. Liu, and L. Tong, "3D texture analysis on MRI images of Alzheimer's disease," Brain imaging and behavior, vol. 6, pp. 61-69, 2012.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 26]


[51] P. Keserwani, V. C. Pammi, O. Prakash, A. Khare, and M. Jeon, "Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region," International Journal of Image, Graphics & Signal Processing, vol. 8, 2016.

[52] 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 2]




References Weight

Web of Science® Citations for all references: 16,225 TCR
SCOPUS® Citations for all references: 16,377 TCR

Web of Science® Average Citations per reference: 306 ACR
SCOPUS® Average Citations per reference: 309 ACR

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

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