Correspondence detection using wavelet-based attribute vectors

Zhong Xue, Dinggang Shen, Christos Davatzikos

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

4 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 using wavelet-based attribute vectors defined on every image voxel. The attribute vector reflects the anatomical characteristics in a large neighborhood around the respective voxel. It plays the role of a morphological signature for each voxel and is therefore made as distinctive as possible. Correspondence is then determined via similarity of attribute vectors. Experiments with brain MR images show that the algorithm performs at least as well as human experts, even for complex cortical structures.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science
EditorsR.E. Ellis, T.M. Peters
Pages762-770
Number of pages9
Volume2879
EditionPART 2
Publication statusPublished - 2003
Externally publishedYes
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings - Montreal, Que., Canada
Duration: 2003 Nov 152003 Nov 18

Other

OtherMedical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings
CountryCanada
CityMontreal, Que.
Period03/11/1503/11/18

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Engineering(all)

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  • Cite this

    Xue, Z., Shen, D., & Davatzikos, C. (2003). Correspondence detection using wavelet-based attribute vectors. In R. E. Ellis, & T. M. Peters (Eds.), Lecture Notes in Computer Science (PART 2 ed., Vol. 2879, pp. 762-770)