Non-rigid registration using gradient of self-similarity response

James L. Huang, Jeffrey J. Rodriguez

Research output: Contribution to journalArticle

Abstract

Locally affine transformation with globally elastic interpolation is a common strategy for non-rigid registration. Current techniques improve the registration accuracy by only processing the sub-images that contain well-defined structures quantified by Moran's spatial correlation. As an indicator, Moran's metric successfully excludes noisy structures that result in misleading global optimum in terms of similarity. However, some well-defined structures with intensity only varying in one direction may also cause mis-registration. In this paper, we propose a new metric based on the response of a similarity function to quantify the ability of being correctly registered for each sub-image. Using receiver operating characteristic analysis, we show that the proposed metric more accurately reflects such ability than Moran's metric. Incorporating the proposed metric into a hierarchical non-rigid registration scheme, we show that registration accuracy is improved relative to Moran's metric.

Original languageEnglish (US)
Pages (from-to)825-834
Number of pages10
JournalImage and Vision Computing
Volume32
Issue number11
DOIs
StatePublished - Nov 2014

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Keywords

  • Hierarchical elastic registration
  • Locally affine transformation
  • Non-rigid registration

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

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