Cluster Computing

, Volume 22, Supplement 6, pp 14081–14089 | Cite as

A voxel based morphometry approach for identifying Alzheimer from MRI images

  • S. SaravanakumarEmail author
  • P. Thangaraj


A voxel based morphometry (VBM) which makes use of a structural brain magnetic resonance imaging (MRI) is now being employed widely for the purpose of assessing the various normal ageing of Alzheimer’s diseases (AD). VBM of the MRI data will contain segmentation within the grey and white matter, the cerebrospinal fluid and its partitions along with that of their anatomical image and its standardization inside the analogous stereotactic region. It further includes the affine transformation with a non-linear warping of the smoothing as well as a statistical investigation. In case there is a cognitive failure that is related to age called Dementia that has been indicated with that of a degeneration of the cortical and the sub-cortical structures. The characterization of such types of morphological changes will help in the understanding of the development of these diseases and the modelling will tend to capture the structural variability of brain which is a valid classification for this disease and its interpretation is found to be quite challenging. Here such features have also been extracted by means of using a curvelet transform along with a principal component analysis (PCA) for this technique of reduction of dimensionality. The Bagging as well as the boosting classifiers have been duly evaluated for their efficiency in classifying dementia. The work will further evaluate the framework by using images from that of the Alzheimer’s disease neuroimaging initiative (ADNI) for identifying dementia. Such results have shown that this classifier proposed has now achieved better accuracy.


Voxel based morphometry (VBM) Magnetic resonance imaging (MRI) Alzheimer’s disease (AD) Curvelet transform Principal component analysis (PCA) Bagging and boosting 


  1. 1.
    Biederer, J.: General requirements of MRI of the lung and suggested standard protocol. (2017). CrossRefGoogle Scholar
  2. 2.
    Papakostas, G.A., Savio, A., Graña, M., Kaburlasos, V.G.: A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data. Neurocomputing 150, 37–42 (2015)CrossRefGoogle Scholar
  3. 3.
    Zhang, W., Song, L., Yin, X., Zhang, J., Liu, C., Wang, J., Lii, H.: Grey matter abnormalities in untreated hyperthyroidism: a voxel-based morphometry study using the DARTEL approach. Eur. J. Radiol. 83(1), e43–e48 (2014)CrossRefGoogle Scholar
  4. 4.
    Zhang, Y., Wang, S., Huo, Y., Wu, L., Liu, A.: Feature extraction of brain MRI by stationary wavelet transform and its applications. J. Biol. Syst. 18(spec01), 115–132 (2010)CrossRefGoogle Scholar
  5. 5.
    Van Der Maaten, L., Postma, E., Van den Herik, J.: Dimensionality reduction: a comparative. J. Mach. Learn Res. 10, 66–71 (2009)Google Scholar
  6. 6.
    Bertrand, H., Perrot, M., Ardon, R., Bloch, I.: Classification of MRI data using deep learning and gaussian process-based model selection. (2017). arXiv preprint arXiv:1701.04355
  7. 7.
    Minhas, S., Khanum, A., Riaz, F., Alvi, A., Khan, S.A.: A non parametric approach for mild cognitive impairment to AD conversion prediction: results on longitudinal data. IEEE J. Biomed. Health Inf. 21, 1403 (2016)CrossRefGoogle Scholar
  8. 8.
    Khedher, L., Ramírez, J., Górriz, J.M., Brahim, A., Segovia, F., Alzheimer's Disease Neuroimaging Initiative: Early diagnosis of Alzheimer's disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing 151, 139–150 (2015)CrossRefGoogle Scholar
  9. 9.
    Kong, Y., Deng, Y., Dai, Q.: Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Process. Lett. 22(5), 573–577 (2015)CrossRefGoogle Scholar
  10. 10.
    Villarini, B., Asaturyan, H., Thomas, E.L., Mould, R., Bell, J.D.: A framework for morphological feature extraction of organs from MR images for detection and classification of abnormalities. In: Proceedings of the 30th IEEE International Symposium on Computer-Based Medical Systems (CBMS) (2017)Google Scholar
  11. 11.
    Selvaraj, D., Dhanasekaran, R.: A review on tissue segmentation and feature extraction of MRI brain images. Int. J. Comput. Sci. Eng. Technol. 4, 1313–1332 (2013)Google Scholar
  12. 12.
    Lemaître, G., Martí, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F.: Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput. Biol. Med. 60, 8–31 (2015)CrossRefGoogle Scholar
  13. 13.
    El-Dahshan, E.S.A., Mohsen, H.M., Revett, K., Salem, A.B.M.: Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014)CrossRefGoogle Scholar
  14. 14.
    Zhu, X., Suk, H.I., Wang, L., Lee, S.W., Shen, D., Alzheimer’s Disease Neuroimaging Initiative: A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med. Image Anal. 38, 205–214 (2017)CrossRefGoogle Scholar
  15. 15.
    Bron, E.E., Steketee, R.M., Houston, G.C., Oliver, R.A., Achterberg, H.C., Loog, M., Klein, S.: Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia. Hum. Brain Mapp. 35(9), 4916–4931 (2014)CrossRefGoogle Scholar
  16. 16.
    Tzalavra, A., Dalakleidi, K., Zacharaki, E.I., Tsiaparas, N., Constantinidis, F., Paragios, N., Nikita, K.S.: Comparison of multi-resolution analysis patterns for texture classification of breast tumors based on DCE-MRI. In: International Workshop on Machine Learning in Medical Imaging, pp. 296–304. Springer International Publishing (2016)Google Scholar
  17. 17.
    Petersen, R.C., Aisen, P.S., Beckett, L.A., Donohue, M.C., Gamst, A.C., Harvey, D.J., Trojanowski, J.Q.: Alzheimer’s disease neuroimaging initiative (ADNI) clinical characterization. Neurology 74(3), 201–209 (2010)CrossRefGoogle Scholar
  18. 18.
    Weiner, M.W., Veitch, D.P., Aisen, P.S., Beckett, L.A., Cairns, N.J., Green, R.C., Morris, J.C.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimer’s Dement. 9(5), e111–e194 (2013)CrossRefGoogle Scholar
  19. 19.
    Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., Initiative, Alzheimer’s Disease Neuroimaging: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104, 398–412 (2015)CrossRefGoogle Scholar
  20. 20.
    Rajakumar, K., Muttan, D.S.: Texture based mri image retrieval using curvelet with statistical similarity matching. IJCSI Int. J. Comput. Sci. Issues 10(2), 483 (2013)Google Scholar
  21. 21.
    Rajakumar, R., Muttan, M.: A framework for MRI image retrieval using curvelet transform and euclidean distance. J. Comput. Sci. 9(3), 285 (2013)CrossRefGoogle Scholar
  22. 22.
    Herrera, L.J., Rojas, I., Pomares, H., Guillén, A., Valenzuela, O., Baños, O.: Classification of MRI images for Alzheimer’s disease detection. In: 2013 International Conference on Social Computing (SocialCom), pp. 846–851. IEEE. (2013)Google Scholar
  23. 23.
    Lama, R.K., Gwak, J., Park, J.S., Lee, S.W.: Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features. J. Healthcare Eng. (2017). CrossRefGoogle Scholar
  24. 24.
    Kumaraswamy, Y.: Performance evaluation of content based image RETRIEVAL for medical images. Indian J. Comput. Sci. Eng. (IJCSE) 4(2), 185–191 (2013)Google Scholar
  25. 25.
    Ramírez, J., Górriz, J.M., Ortiz, A., Padilla, P., Martínez-Murcia, F.J., Alzheimer Disease Neuroimaging Initiative: Ensemble tree learning techniques for magnetic resonance image analysis. In: Innovation in Medicine and Healthcare 2015, pp. 395–404. Springer, Cham (2016)Google Scholar
  26. 26.
    Gray, K.R.: Machine learning for image-based classification of Alzheimer’s disease (2012)Google Scholar
  27. 27.
    Ramírez, J., Górriz, J.M., Martínez-Murcia, F.J., Segovia, F., Salas-Gonzalez, D.: Magnetic resonance image classification using nonnegative matrix factorization and ensemble tree learning techniques. In: 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–5. IEEE (2016)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAdithya Institute of TechnologyCoimbatoreIndia
  2. 2.Department of Computer Science and EngineeringBannari Amman Institute of TechnologySathyamangalamIndia

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