A generalized learning based framework for fast brain image registration.

Minjeong Kim, Guorong Wu, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

This paper presents a generalized learning based framework for improving both speed and accuracy of the existing deformable registration method. The key of our framework involves the utilization of a support vector regression (SVR) to learn the correlation between brain image appearances and their corresponding shape deformations to a template, for helping significantly cut down the computation cost and improve the robustness to local minima by using the learned correlation to instantly predict a good subject-specific deformation initialization for any given subject under registration. Our framework consists of three major parts: 1) training of SVR models based on the statistics of image samples and their shape deformations to capture intrinsic image-deformation correlations, 2) deformation prediction for a new subject with the trained SVR models to generate a subject-resemblance intermediate template by warping the original template with the predicted deformations, and 3) estimating of the residual deformation from the intermediate template to the subject for refined registration. Any existing deformable registration methods can be easily employed for training the SVR models and estimating the residual deformation. We have tested in this paper the two widely used deformable registration algorithms, i.e., HAMMER] and diffeomorphic demons, for demonstration of our proposed frameowrk. Experimental results show that, compared to the registration using the original methods (with no deformation prediction), our framework achieves a significant speedup (6X faster than HAMMER, and 3X faster than diffeomorphic demons), while maintaining comparable (or even slighly better) registration accuracy.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages306-314
Number of pages9
Volume13
EditionPt 2
Publication statusPublished - 2010 Nov 18

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Training Support
Learning
Brain
Costs and Cost Analysis

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Kim, M., Wu, G., Yap, P. T., & Shen, D. (2010). A generalized learning based framework for fast brain image registration. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 13, pp. 306-314)

A generalized learning based framework for fast brain image registration. / Kim, Minjeong; Wu, Guorong; Yap, Pew Thian; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 2. ed. 2010. p. 306-314.

Research output: Chapter in Book/Report/Conference proceedingChapter

Kim, M, Wu, G, Yap, PT & Shen, D 2010, A generalized learning based framework for fast brain image registration. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 13, pp. 306-314.
Kim M, Wu G, Yap PT, Shen D. A generalized learning based framework for fast brain image registration. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 13. 2010. p. 306-314
Kim, Minjeong ; Wu, Guorong ; Yap, Pew Thian ; Shen, Dinggang. / A generalized learning based framework for fast brain image registration. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 2. ed. 2010. pp. 306-314
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