Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels

Zhong Xue, Dinggang Shen, Christos Davatzikos

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) denned on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.

Original languageEnglish
Pages (from-to)1276-1291
Number of pages16
JournalIEEE Transactions on Medical Imaging
Volume23
Issue number10
DOIs
Publication statusPublished - 2004 Oct 1
Externally publishedYes

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Keywords

  • Computational anatomy
  • Correspondence
  • Deformable registration
  • Image matching
  • Wavelet transformations

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics

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