Tree-Guided Sparse Coding for Brain Disease Classification

  • Manhua Liu
  • Daoqiang Zhang
  • Pew-Thian Yap
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)


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. Sparsecoding 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.


Support Vector Machine Mild Cognitive Impairment Class Label Sparse Code Group Lasso 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Manhua Liu
    • 1
    • 2
  • Daoqiang Zhang
    • 1
    • 3
  • Pew-Thian Yap
    • 1
  • Dinggang Shen
    • 1
  1. 1.IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  2. 2.Department of Instrument Science and TechnologyShanghai Jiao Tong UniversityChina
  3. 3.Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsChina

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