Initialising groupwise non-rigid registration using multiple parts+geometry models.

Pei Zhang, Pew Thian Yap, Dinggang Shen, Timothy F. Cootes

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Groupwise non-rigid registration is an important technique in medical image analysis. Recent studies show that its accuracy can be greatly improved by explicitly providing good initialisation. This is achieved by seeking a sparse correspondence using a parts+geometry model. In this paper we show that a single parts+geometry model is unlikely to establish consistent sparse correspondence for complex objects, and that better initialisation can be achieved using a set of models. We describe how to combine the strengths of multiple models, and demonstrate that the method gives state-of-the-art performance on three datasets, with the most significant improvement on the most challenging.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages156-163
Number of pages8
Volume15
EditionPt 3
Publication statusPublished - 2012 Dec 1

ASJC Scopus subject areas

  • Medicine(all)

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    Zhang, P., Yap, P. T., Shen, D., & Cootes, T. F. (2012). Initialising groupwise non-rigid registration using multiple parts+geometry models. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 15, pp. 156-163)