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Proceedings Paper

Fully automatic hybrid registration method based on point feature detection without user intervention
Author(s): Bang-Bon Koo; Jong-Min Lee; June-Sic Kim; In-Young Kim; Jun-Soo Kwon; Sun I. Kim
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Paper Abstract

In earlier work (KIM, J.S, MBEC, 2003), we demonstrated the registration method with a non-linear transformation using intensity similarity and feature similarity. Although the former approach showed good match in global shape of brain and feature-defined region, method contains user interventions for defining appropriate and sufficient number features. While manual delineating the region of interests for sufficient number of feature is a very time-consuming and can provide intra-, inter-rater variability, we proposed fully automatic hybrid registration via automatic feature defining method. Automatic feature definition was performed on the cortical surface from CLASP (KIM, J.S, Neuroimage, 2005) with using cortical surface matching algorithm (Robbins, S., MIA, 2004) and then applied to hybrid registration. The object of this work is to develop fully automated hybrid registration method which reveals enhanced performance in comparison to previous automated registration methods. In the result, our proposed scheme showed efficient performance from maintaining the strong points of hybrid registration without any user intervention.

Paper Details

Date Published: 10 March 2006
PDF: 8 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61442N (10 March 2006); doi: 10.1117/12.652935
Show Author Affiliations
Bang-Bon Koo, Hanyang Univ. (South Korea)
Jong-Min Lee, Hanyang Univ. (South Korea)
June-Sic Kim, Seoul National Univ. College of Medicine (South Korea)
In-Young Kim, Hanyang Univ. (South Korea)
Jun-Soo Kwon, Seoul National Univ. College of Medicine (South Korea)
Sun I. Kim, Hanyang Univ. (South Korea)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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