Tree-guided sparse coding for brain disease classification

Manhua Liu, Daoqiang Zhang, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

Neuroimage analysis based on machine learning technologies has been widely employed to assist the diagnosis of brain diseases such as Alzheimer's disease and its prodromal stage - mild cognitive impairment. One of the major problems in brain image analysis involves learning the most relevant features from a huge set of raw imaging features, which are far more numerous than the training samples. This makes the tasks of both disease classification and interpretation extremely challenging. Sparse coding via L1 regularization, such as Lasso, can provide an effective way to select the most relevant features for alleviating the curse of dimensionality and achieving more accurate classification. However, the selected features may distribute randomly throughout the whole brain, although in reality disease-induced abnormal changes often happen in a few contiguous regions. To address this issue, we investigate a tree-guided sparse coding method to identify grouped imaging features in the brain regions for guiding disease classification and interpretation. Spatial relationships of the image structures are imposed during sparse coding with a tree-guided regularization. Our experimental results on the ADNI dataset show that the tree-guided sparse coding method not only achieves better classification accuracy, but also allows for more meaningful diagnosis of brain diseases compared with the conventional L1-regularized Lasso.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
EditorsNicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
PublisherSpringer Verlag
Pages239-247
Number of pages9
ISBN (Print)9783642334535
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 2012 Oct 12012 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7512 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period12/10/112/10/5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, M., Zhang, D., Yap, P. T., & Shen, D. (2012). Tree-guided sparse coding for brain disease classification. In N. Ayache, H. Delingette, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings (pp. 239-247). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7512 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33454-2_30