Learning-based deformable registration of MR brain images

Guorong Wu, Feihu Qi, Dinggang Shen

Research output: Contribution to journalArticle

78 Citations (Scopus)

Abstract

This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence detection during the registration procedure. This is achieved by optimizing an energy function that requires each point to have its best-scale geometric features consistent over the corresponding points in the training samples, and at the same time distinctive from those of nearby points in the neighborhood. Second, the active points used to drive the brain registration are hierarchically selected during the registration procedure, based on their saliency and consistency measures. That is, the image points with salient and consistent features (across different individuals) are considered for the initial registration of two images, while other less salient and consistent points join the registration procedure later. By incorporating these two novel strategies into the framework of the HAMMER registration algorithm, the registration accuracy has been improved according to the results on simulated brain data, and also visible improvement is observed particularly in the cortical regions of real brain data.

Original languageEnglish
Article number1677721
Pages (from-to)1145-1157
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume25
Issue number9
DOIs
Publication statusPublished - 2006 Sep 1
Externally publishedYes

Keywords

  • Best features
  • Best scale selection
  • Consistency measurement
  • Deformable registration
  • Feature-based registration
  • Hierarchical registration
  • Learning-based method
  • Saliency measurement

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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