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

doi:10.1117/12.652935

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 +

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 61442N (2006) https://doi.org/10.1117/12.652935
Event: Medical Imaging, 2006, San Diego, California, United States

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE