Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Images

  • Jun Zhang
  • Mingxia Liu
  • Le An
  • Yaozong Gao
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10081)


In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) High-level statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.


  1. 1.
    Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)CrossRefGoogle Scholar
  2. 2.
    Thung, K.H., Wee, C.Y., Yap, P.T., Shen, D., Initiative, A.D.N., et al.: Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. NeuroImage 91, 386–400 (2014)CrossRefGoogle Scholar
  3. 3.
    Thung, K.-H., Yap, P.-T., Adeli-M, E., Shen, D.: Joint diagnosis and conversion time prediction of progressive mild cognitive impairment (PMCI) using low-rank subspace clustering and matrix completion. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 527–534. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_63 CrossRefGoogle Scholar
  4. 4.
    Liu, M., Zhang, D., Shen, D.: View-centralized multi-atlas classification for Alzheimer’s disease diagnosis. Hum. Brain Mapp. 36(5), 1847–1865 (2015)CrossRefGoogle Scholar
  5. 5.
    Liu, M., Zhang, D., Adeli-Mosabbeb, E., Shen, D.: Inherent structure based multi-view learning with multi-template feature representation for Alzheimer’s disease diagnosis. IEEE Trans. Biomed. Eng. 63(7), 1473–1482 (2016)CrossRefGoogle Scholar
  6. 6.
    Liu, M., Zhang, D., Shen, D.: Relationship induced multi-template learning for diagnosis of Alzheimer’s disease and mild cognitive impairment. IEEE Trans. Med. Imaging 35(6), 1463–1474 (2016)CrossRefGoogle Scholar
  7. 7.
    Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M.K., Johnson, S.C., Initiative, A.D.N., et al.: Spatially augmented lpboosting for ad classification with evaluations on the adni dataset. Neuroimage 48(1), 138–149 (2009)CrossRefGoogle Scholar
  8. 8.
    Zhu, X., Suk, H.I., Shen, D.: A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage 100, 91–105 (2014)CrossRefGoogle Scholar
  9. 9.
    Zhu, X., Suk, H.I., Lee, S.W., Shen, D.: Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis. Brain Imaging Behav. 10(3), 1–11 (2015)Google Scholar
  10. 10.
    Zhu, X., Suk, H.I., Lee, S.W., Shen, D.: Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607–618 (2016)CrossRefGoogle Scholar
  11. 11.
    Gerardin, E., Chételat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H.S., Niethammer, M., Dubois, B., Lehéricy, S., Garnero, L., et al.: Multidimensional classification of hippocampal shape features discriminates alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47(4), 1476–1486 (2009)CrossRefGoogle Scholar
  12. 12.
    Gao, Y., Adeli-M., E., Kim, M., Giannakopoulos, P., Haller, S., Shen, D.: Medical image retrieval using multi-graph learning for MCI diagnostic assistance. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 86–93. Springer, Cham (2015). doi: 10.1007/978-3-319-24571-3_11 CrossRefGoogle Scholar
  13. 13.
    Gao, Y., Wee, C.-Y., Kim, M., Giannakopoulos, P., Montandon, M.-L., Haller, S., Shen, D.: MCI identification by joint learning on multiple MRI data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 78–85. Springer, Cham (2015). doi: 10.1007/978-3-319-24571-3_10 CrossRefGoogle Scholar
  14. 14.
    Klöppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, C.R., Ashburner, J., Frackowiak, R.S.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3), 681–689 (2008)CrossRefGoogle Scholar
  15. 15.
    Chincarini, A., Sensi, F., Rei, L., Gemme, G., Squarcia, S., Longo, R., Brun, F., Tangaro, S., Bellotti, R., Amoroso, N., et al.: Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer’s disease. NeuroImage 125, 834–847 (2016)CrossRefGoogle Scholar
  16. 16.
    Jack, C., Shiung, M., Gunter, J., Obrien, P., Weigand, S., Knopman, D.S., Boeve, B.F., Ivnik, R.J., Smith, G.E., Cha, R., et al.: Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 62(4), 591–600 (2004)CrossRefGoogle Scholar
  17. 17.
    Aguilar, C., Muehlboeck, J.S., Mecocci, P., Vellas, B., Tsolaki, M., Kloszewska, I., Soininen, H., Lovestone, S., Wahlund, L.O., Simmons, A., et al.: Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the addneuromed cohort. Front. Aging Neurosci. 6, 145 (2014)CrossRefGoogle Scholar
  18. 18.
    Thung, K.H., Wee, C.Y., Yap, P.T., Shen, D.: Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans. Brain Struct. Funct. 1–17 (2015)Google Scholar
  19. 19.
    Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., Evans, A.C.: Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22(2), 324–333 (1998)CrossRefGoogle Scholar
  20. 20.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005. CVPR 2005, pp. 886–893. IEEE (2005)Google Scholar
  21. 21.
    Mardia, K.: Assessment of multinormality and the robustness of Hotelling’s T\(^2\) test. Appl. Stat. 24, 163–171 (1975)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zhang, J., Gao, Y., Gao, Y., Brent, M., Shen, D.: Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis. IEEE Trans. Med. Imaging 35(12), 2524–2533 (2016)Google Scholar
  23. 23.
    Gao, Y., Shen, D.: Context-aware anatomical landmark detection: application to deformable model initialization in prostate CT images. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 165–173. Springer, Cham (2014). doi: 10.1007/978-3-319-10581-9_21 Google Scholar
  24. 24.
    Zhang, J., Gao, Y., Wang, L., Tang, Z., Xia, J.J., Shen, D.: Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model and multi-scale statistical features. IEEE Trans. Biomed. Eng. 63(9), 1820–1829 (2016)Google Scholar
  25. 25.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vision 43(1), 29–44 (2001)CrossRefzbMATHGoogle Scholar
  26. 26.
    Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006). doi: 10.1007/11744085_38 CrossRefGoogle Scholar
  27. 27.
    Yang, J., Jiang, Y.G., Hauptmann, A.G., Ngo, C.W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Multimedia Information Retrieval, pp. 197–206. ACM (2007)Google Scholar
  28. 28.
    Jiang, Y.G., Ngo, C.W., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, ACM 494–501(2007)Google Scholar
  29. 29.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A k-means clustering algorithm. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 28(1), 100–108 (1979)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jun Zhang
    • 1
  • Mingxia Liu
    • 1
  • Le An
    • 1
  • Yaozong Gao
    • 1
    • 2
  • Dinggang Shen
    • 1
    • 3
    Email author
  1. 1.Department of Radiology and BRICUNC at Chapel HillChapel HillUSA
  2. 2.Department of Computer ScienceUNC at Chapel HillChapel HillUSA
  3. 3.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

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