Paper
10 March 2006 Fully automatic hybrid registration method based on point feature detection without user intervention
Bang-Bon Koo, Jong-Min Lee, June-Sic Kim, In-Young Kim, Jun-Soo Kwon, Sun I. Kim
Author Affiliations +
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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bang-Bon Koo, Jong-Min Lee, June-Sic Kim, In-Young Kim, Jun-Soo Kwon, and Sun I. Kim "Fully automatic hybrid registration method based on point feature detection without user intervention", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61442N (10 March 2006); https://doi.org/10.1117/12.652935
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Brain

Image registration

3D modeling

Feature extraction

Tissues

Image analysis

Magnetic resonance imaging

Back to Top