Non-rigid registration and correspondence finding in medical image analysis using multiple-layer flexible mesh template matching

Jianhua Yao, Russell Taylor

Research output: Contribution to journalArticle

Abstract

In this paper we present a novel technique for non-rigid medical image registration and correspondence finding based on a multiple-layer flexible mesh template matching technique. A statistical anatomical model is built in the form of a tetrahedral mesh, which incorporates both shape and density properties of the anatomical structure. After the affine transformation and global deformation of the model are computed by optimizing an energy function, a multiple-layer flexible mesh template matching is applied to find the vertex correspondence and achieve local deformation. The multiple-layer structure of the template can be used to describe different scale of anatomical features; furthermore, the template matching is flexible which makes the correspondence finding robust. A leave-one-out validation has been conducted to demonstrate the effectiveness and accuracy of our method.

Original languageEnglish (US)
Pages (from-to)1145-1165
Number of pages21
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume17
Issue number7
DOIs
StatePublished - Nov 1 2003

Keywords

  • Correspondence
  • Multiple-layer flexible mesh template
  • Non-rigid registration
  • Statistical model

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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