Research Article
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Year 2016, Volume: 4 Issue: Special Issue-1, 195 - 198, 26.12.2016

Abstract

References

  • [1] C. R. Jack Jr., M. A. Bernstein, N. C. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P. J. Britson, J. L. Whitwell, C. Ward, A. M. Dale, J. P. Felmlee, J. L. Gunter, D. L. G. Hill, R. Killiany, N. Schuff, S. Fox-Bosetti, C. Lin, C. Studholme, C. S. DeCarli, G. Krueger, H. A. Ward, G. J. Metzger, K. T. Scott, R. Mallozzi, D. Blezek, J. Levy, J. P. Debbins, A. S. Fleisher, M. Albert, R. Green, G. Bartzokis, G. Glover, J. Mugler, M. W. Weiner, “The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods,” Journal of Magnetic Resonance Imaging, vol. 27, no. 4, pp. 685-691, 2008.
  • [2] R. C. Petersen, “Mild cognitive impairment as a diagnostic entity,” Journal of Internal Medicine, vol. 256, no. 3, pp. 183-194, 2004.
  • [3] P. M. Thompson, K. M. Hayashi, G. De Zubicaray, A. L. Janke, S. E. Rose, J. Semple, D. Herman, M. S. Hong, S. S. Dittmer, D. M. Doddrell, A. W. Toga, “Dynamics of gray matter loss in Alzheimer's disease,” Journal of Neuroscience, vol. 23, no. 3, pp. 994-1005, 2003.
  • [4] A. Demirhan, T. M. Nir, A. Zavaliangos-Petropulu, C. R. Jack Jr., W. M. Weiner, M. A. Bernstein, P. M. Thompson, N. Jahanshad, “Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images,” in Proceedings - International Symposium on Biomedical Imaging, 2015, art. no. 7163832, pp. 126-130.
  • [5] G. B. Frisoni, N. C. Fox, C. R. Jack Jr., P. Scheltens, P. M. Thompson, “The clinical use of structural MRI in Alzheimer disease,” Nature Reviews Neurology, vol. 6, no. 2, pp. 67-77, 2010.
  • [6] M. R. Sabuncu, E. Konukoglu, “Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study,” Neuroinformatics, 13 (1), pp. 31-46, 2015.
  • [7] B. Magnin, L. Mesrob, S. Kinkingnéhun, M. Pélégrini-Issac, O. Colliot, M. Sarazin, B. Dubois, S. Lehéricy, H. Benali, “Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI,” Neuroradiology, vol. 51, no. 2, pp. 73-83, 2009.
  • [8] C. A. Cocosco, A. P. Zijdenbos, A. C. Evans, “A fully automatic and robust brain MRI tissue classification method,” Medical Image Analysis, vol. 7, no. 4, pp. 513-527, Dec. 2003.
  • [9] N. Amoroso, R. Errico, R. Bellotti, “PRISMA-CAD: Fully automated method for Computer-Aided Diagnosis of Dementia based on structural MRI data,” in Proc MICCAI Workshop Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data, 2014, pp. 16–23.
  • [10] D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, & R. L. Buckner, “Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,” Journal of Cognitive Neuroscience, vol. 19, no. 9, pp. 1498–1507, Sep. 2007.
  • [11] R. Casanova, F.-C. Hsu, & M. A. Espeland, Alzheimer’s Disease Neuroimaging Initiative, “Classification of Structural MRI Images in Alzheimer’s Disease from the Perspective of ill-Posed Problems,” PLoS One, vol. 7, no. 10, e44877, 2012.
  • [12] J. P. Vert, K. Tsuda, B. Schölkopf, “A primer on kernel methods,” in Kernel Methods in Computational Biology, 2nd ed., Cambridge, MA: MIT Press, 2004.
  • [13] J. Ramírez, J. M. Górriz, D. Salas-Gonzalez, A. Romero, M. López, I. Álvarez, M. Gómez-Río, “Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features,” Information Sciences, vol. 237, no. 10, pp. 59-72, Jul. 2013.
  • [14] A. Demirhan, Y. A. Kılıç, İ. Güler, “Artificial Intelligence Applications in Medicine,” Turkish Journal of Intensive Care Medicine, vol. 9, no. 1, pp. 31-41, 2010.
  • [15] Y. Zhang, Z. Dong, L. Wu, S. Wang, “A hybrid method for MRI brain image classification,” Expert Systems with Applications, vol. 38, no. 8, pp. 10049-10053, Aug. 2011.
  • [16] C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A practical guide to support vector classification,” Department of Computer Science, National Taiwan University, Taipei, Taiwan, Tech. Rep., 2003.

Classification of Structural MRI for Detecting Alzheimer’s Disease

Year 2016, Volume: 4 Issue: Special Issue-1, 195 - 198, 26.12.2016

Abstract

Alzheimer’s Disease (AD) is a pathological form of dementia that degenerates
brain structures. AD affects millions of elderly people over the world and the
number of people with AD doubles every year. Detecting AD years before the
effects of disease using structural magnetic resonance imaging (MRI) of the
brain is possible. Neuroimaging features that are extracted from the structural
brain MRI can be used to predict AD by revealing disease related patterns.
Machine learning techniques can detect AD and predict conversions from mild
cognitive impairment (MCI) to AD automatically and successfully by using these
neuroimaging features. In this study common structural brain measures such as
volumes and thickness of anatomical structures that are obtained from The Open
Access Series of Imaging Studies (OASIS) and made publicly available by
https://www.nmr.mgh.harvard.edu/lab/mripredict are analysed. State-of-the-art
machine learning techniques, namely support vector machines (SVM), k-nearest
neighbour (kNN) algorithm and backpropagation neural network (BP-NN) are
employed to discriminate AD and mild AD from healthy controls. Training
hyperparameters of the classifiers are tuned using classification accuracy
which is obtained with 5-fold cross validation. Prediction performance of the
techniques are compared using accuracy, sensitivity and specificity. Results of
the system revealed that AD can be distinguished from the healthy controls
successfully using multivariate morphological features and machine learning
tools. According to the performed experiments SVM is the most successful
classifier for detecting AD with classification accuracies up to 82%.

References

  • [1] C. R. Jack Jr., M. A. Bernstein, N. C. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P. J. Britson, J. L. Whitwell, C. Ward, A. M. Dale, J. P. Felmlee, J. L. Gunter, D. L. G. Hill, R. Killiany, N. Schuff, S. Fox-Bosetti, C. Lin, C. Studholme, C. S. DeCarli, G. Krueger, H. A. Ward, G. J. Metzger, K. T. Scott, R. Mallozzi, D. Blezek, J. Levy, J. P. Debbins, A. S. Fleisher, M. Albert, R. Green, G. Bartzokis, G. Glover, J. Mugler, M. W. Weiner, “The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods,” Journal of Magnetic Resonance Imaging, vol. 27, no. 4, pp. 685-691, 2008.
  • [2] R. C. Petersen, “Mild cognitive impairment as a diagnostic entity,” Journal of Internal Medicine, vol. 256, no. 3, pp. 183-194, 2004.
  • [3] P. M. Thompson, K. M. Hayashi, G. De Zubicaray, A. L. Janke, S. E. Rose, J. Semple, D. Herman, M. S. Hong, S. S. Dittmer, D. M. Doddrell, A. W. Toga, “Dynamics of gray matter loss in Alzheimer's disease,” Journal of Neuroscience, vol. 23, no. 3, pp. 994-1005, 2003.
  • [4] A. Demirhan, T. M. Nir, A. Zavaliangos-Petropulu, C. R. Jack Jr., W. M. Weiner, M. A. Bernstein, P. M. Thompson, N. Jahanshad, “Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images,” in Proceedings - International Symposium on Biomedical Imaging, 2015, art. no. 7163832, pp. 126-130.
  • [5] G. B. Frisoni, N. C. Fox, C. R. Jack Jr., P. Scheltens, P. M. Thompson, “The clinical use of structural MRI in Alzheimer disease,” Nature Reviews Neurology, vol. 6, no. 2, pp. 67-77, 2010.
  • [6] M. R. Sabuncu, E. Konukoglu, “Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study,” Neuroinformatics, 13 (1), pp. 31-46, 2015.
  • [7] B. Magnin, L. Mesrob, S. Kinkingnéhun, M. Pélégrini-Issac, O. Colliot, M. Sarazin, B. Dubois, S. Lehéricy, H. Benali, “Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI,” Neuroradiology, vol. 51, no. 2, pp. 73-83, 2009.
  • [8] C. A. Cocosco, A. P. Zijdenbos, A. C. Evans, “A fully automatic and robust brain MRI tissue classification method,” Medical Image Analysis, vol. 7, no. 4, pp. 513-527, Dec. 2003.
  • [9] N. Amoroso, R. Errico, R. Bellotti, “PRISMA-CAD: Fully automated method for Computer-Aided Diagnosis of Dementia based on structural MRI data,” in Proc MICCAI Workshop Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data, 2014, pp. 16–23.
  • [10] D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, & R. L. Buckner, “Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,” Journal of Cognitive Neuroscience, vol. 19, no. 9, pp. 1498–1507, Sep. 2007.
  • [11] R. Casanova, F.-C. Hsu, & M. A. Espeland, Alzheimer’s Disease Neuroimaging Initiative, “Classification of Structural MRI Images in Alzheimer’s Disease from the Perspective of ill-Posed Problems,” PLoS One, vol. 7, no. 10, e44877, 2012.
  • [12] J. P. Vert, K. Tsuda, B. Schölkopf, “A primer on kernel methods,” in Kernel Methods in Computational Biology, 2nd ed., Cambridge, MA: MIT Press, 2004.
  • [13] J. Ramírez, J. M. Górriz, D. Salas-Gonzalez, A. Romero, M. López, I. Álvarez, M. Gómez-Río, “Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features,” Information Sciences, vol. 237, no. 10, pp. 59-72, Jul. 2013.
  • [14] A. Demirhan, Y. A. Kılıç, İ. Güler, “Artificial Intelligence Applications in Medicine,” Turkish Journal of Intensive Care Medicine, vol. 9, no. 1, pp. 31-41, 2010.
  • [15] Y. Zhang, Z. Dong, L. Wu, S. Wang, “A hybrid method for MRI brain image classification,” Expert Systems with Applications, vol. 38, no. 8, pp. 10049-10053, Aug. 2011.
  • [16] C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A practical guide to support vector classification,” Department of Computer Science, National Taiwan University, Taipei, Taiwan, Tech. Rep., 2003.
There are 16 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Ayşe Demirhan

Publication Date December 26, 2016
Published in Issue Year 2016 Volume: 4 Issue: Special Issue-1

Cite

APA Demirhan, A. (2016). Classification of Structural MRI for Detecting Alzheimer’s Disease. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 195-198. https://doi.org/10.18201/ijisae.270708
AMA Demirhan A. Classification of Structural MRI for Detecting Alzheimer’s Disease. International Journal of Intelligent Systems and Applications in Engineering. December 2016;4(Special Issue-1):195-198. doi:10.18201/ijisae.270708
Chicago Demirhan, Ayşe. “Classification of Structural MRI for Detecting Alzheimer’s Disease”. International Journal of Intelligent Systems and Applications in Engineering 4, no. Special Issue-1 (December 2016): 195-98. https://doi.org/10.18201/ijisae.270708.
EndNote Demirhan A (December 1, 2016) Classification of Structural MRI for Detecting Alzheimer’s Disease. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 195–198.
IEEE A. Demirhan, “Classification of Structural MRI for Detecting Alzheimer’s Disease”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, pp. 195–198, 2016, doi: 10.18201/ijisae.270708.
ISNAD Demirhan, Ayşe. “Classification of Structural MRI for Detecting Alzheimer’s Disease”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (December 2016), 195-198. https://doi.org/10.18201/ijisae.270708.
JAMA Demirhan A. Classification of Structural MRI for Detecting Alzheimer’s Disease. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:195–198.
MLA Demirhan, Ayşe. “Classification of Structural MRI for Detecting Alzheimer’s Disease”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, 2016, pp. 195-8, doi:10.18201/ijisae.270708.
Vancouver Demirhan A. Classification of Structural MRI for Detecting Alzheimer’s Disease. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):195-8.