Integrated feature extraction and selection for neuroimage classification

Yong Fan, Dinggang Shen

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

7 Citations (Scopus)

Abstract

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Original languageEnglish
Title of host publicationMedical Imaging 2009 - Image Processing
DOIs
Publication statusPublished - 2009
EventMedical Imaging 2009 - Image Processing - Lake Buena Vista, FL, United States
Duration: 2009 Feb 82009 Feb 10

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7259
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2009 - Image Processing
CountryUnited States
CityLake Buena Vista, FL
Period09/2/809/2/10

Keywords

  • Feature extraction
  • Feature selection
  • Neuroimage classification
  • Pattern recognition
  • Statistical methods

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Fan, Y., & Shen, D. (2009). Integrated feature extraction and selection for neuroimage classification. In Medical Imaging 2009 - Image Processing [72591U] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7259). https://doi.org/10.1117/12.811781