Applications of Wavelets in Morphometric Analysis of Medical Images

Christos Davatzikos, Xiaodong Tao, Dinggang Shen

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)


Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

Original languageEnglish
Pages (from-to)435-444
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue number1
Publication statusPublished - 2003
Externally publishedYes
EventWavelets: Applications in Signal and Image Processing X - San Diego, CA, United States
Duration: 2003 Aug 42003 Aug 8


  • Active shape model
  • Attribute vector
  • Morphological analysis
  • Morphological classification
  • Wavelet transform

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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