Improve brain registration using machine learning methods

Guorong Wu, Feihu Qi, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A machine learning method is introduced here to improve the accuracy of brain registration. Generally, different brain regions might need different types or sets of features for registration, which actually can be determined and learned from the brain samples by a machine learning method. In this paper, we focus on investigating the best geometric features required by different brain regions, to match the correspondences and manage the registration procedure hierarchically. Compared to other conventional registration methods where no learning method is employed, our learning-based registration method is able to produce not only more consistent registration on serial images of the same subject, but also more accurate registration on simulated dataset.

Original languageEnglish
Title of host publication2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings
Pages434-437
Number of pages4
Volume2006
Publication statusPublished - 2006 Nov 17
Externally publishedYes
Event2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Arlington, VA, United States
Duration: 2006 Apr 62006 Apr 9

Other

Other2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro
CountryUnited States
CityArlington, VA
Period06/4/606/4/9

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Wu, G., Qi, F., & Shen, D. (2006). Improve brain registration using machine learning methods. In 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings (Vol. 2006, pp. 434-437). [1624946]