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Supervised Block Sparse Dictionary Learning for Simultaneous Clustering and Classification in Computational Anatomy

  • Erdem Varol
  • Christos Davatzikos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

An important prerequisite for computational neuroanatomy is the spatial normalization of the data. Despite its importance for the success of the subsequent statistical analysis, image alignment is dealt with from the perspective of image matching, while its influence on the group analysis is neglected. The choice of the template, the registration algorithm as well as the registration parameters, all confound group differences and impact the outcome of the analysis. In order to limit their influence, we perform multiple registrations by varying these parameters, resulting in multiple instances for each sample. In order to harness the high dimensionality of the data and emphasize the group differences, we propose a supervised dimensionality reduction technique that takes into account the organization of the data. This is achieved by solving a supervised dictionary learning problem for block-sparse signals. Structured sparsity allows the grouping of instances across different independent samples, while label supervision allows for discriminative dictionaries. The block structure of dictionaries allows constructing multiple classifiers that treat each dictionary block as a basis of a subspace that spans a separate band of information. We formulate this problem as a convex optimization problem with a geometric programming (GP) component. Promising results that demonstrate the potential of the proposed approach are shown for an MR image dataset of Autism subjects.

Keywords

Autism Spectrum Disorder Multiple Instance Dictionary Learning Geometric Programming Multiple Instance Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 561–568 (2003)Google Scholar
  2. 2.
    Ashburner, J., Friston, K.J.: Voxel-based morphometry - the methods. Neuroimage 11(6), 805–821 (2000)CrossRefGoogle Scholar
  3. 3.
    Batmanghelich, N.K., Taskar, B., Davatzikos, C.: Generative-discriminative basis learning for medical imaging. IEEE Transactions on Medical Imaging 31(1), 51–69 (2012)CrossRefGoogle Scholar
  4. 4.
    Davatzikos, C., Genc, A., Xu, D., Resnick, S.M.: Voxel-based morphometry using the ravens maps: methods and validation using simulated longitudinal atrophy. NeuroImage 14(6), 1361–1369 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Eldar, Y.C., Kuppinger, P., Bolcskei, H.: Block-sparse signals: Uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing 58(6), 3042–3054 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Elhamifar, E., Vidal, R.: Sparse subspace clustering: Algorithm, theory, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(11), 2765–2781 (2013)CrossRefGoogle Scholar
  7. 7.
    Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C.: Compare: classification of morphological patterns using adaptive regional elements. IEEE Transactions on Medical Imaging 26(1), 93–105 (2007)CrossRefGoogle Scholar
  8. 8.
    Halko, N., Martinsson, P.G., Tropp, J.A.: Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review 53(2), 217–288 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Mairal, J., Bach, F., Ponce, J.: Task-driven dictionary learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(4) (2012)Google Scholar
  10. 10.
    Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: Dramms: Deformable registration via attribute matching and mutual-saliency weighting. Medical Image Analysis 15(4), 622–639 (2011)CrossRefGoogle Scholar
  11. 11.
    Varol, E., Gaonkar, B., Davatzikos, C.: Classifying medical images using morphological appearance manifolds. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 744–747. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Erdem Varol
    • 1
  • Christos Davatzikos
    • 1
  1. 1.University of PennsylvaniaUSA

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