Local joint entropy based non-rigid multimodality image registration

Yu Han, Xiang Chu Feng, George Baciu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Variational-based image registration is an important research topic in the field of pattern recognition. Classical models for this task usually use mutual information to measure the similarity of the images to be aligned. Although these models can generate good registration results for rigid deformation, they do not perform well for non-rigid registration because of the complexity of local deformation. In this paper, we propose a novel model to solve the problem of non-rigid registration of multimodality images. In the model, the local joint entropy is introduced to measure the similarity of the images to be aligned, and the weighted Horn-type regularizer is used to protect the displacement field to be estimated from over-smoothing. The proposed model has the advantage of aligning local edges of noise-free images better than the model based on mutual information and total variation, and the free-form deformation model. Furthermore, the proposed weighted regularizer is more robust than the classical total variation regularizer and the Horn-type regularizer in the alignment of noisy images. By using the alternative minimization method, we design a fast iteration algorithm to solve our model. Numerical results show the promising performance of our registration method.
Original languageEnglish
Pages (from-to)1405-1415
Number of pages11
JournalPattern Recognition Letters
Volume34
Issue number12
DOIs
Publication statusPublished - 14 Jun 2013

Keywords

  • Alternative minimization
  • AOS algorithm
  • Image registration
  • Non-rigid
  • Variational differential
  • Weighted Horn regularization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this