Landmark-driven parameter optimization for non-linear image registration

Alexander Schmidt-Richberg; René Werner; Jan Ehrhardt; Jan-Christoph Wolf; Heinz Handels

doi:10.1117/12.877059

11 March 2011 Landmark-driven parameter optimization for non-linear image registration

Alexander Schmidt-Richberg, René Werner, Jan Ehrhardt, Jan-Christoph Wolf, Heinz Handels

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79620T (2011) https://doi.org/10.1117/12.877059
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Image registration is one of the most common research areas in medical image processing. It is required for example for image fusion, motion estimation, patient positioning, or generation of medical atlases. In most intensity-based registration approaches, parameters have to be determined, most commonly a parameter indicating to which extend the transformation is required to be smooth. Its optimal value depends on multiple factors like the application and the occurrence of noise in the images, and may therefore vary from case to case. Moreover, multi-scale approaches are commonly applied on registration problems and demand for further adjustment of the parameters. In this paper, we present a landmark-based approach for automatic parameter optimization in non-linear intensity-based image registration. In a first step, corresponding landmarks are automatically detected in the images to match. The landmark-based target registration error (TRE), which is shown to be a valid metric for quantifying registration accuracy, is then used to optimize the parameter choice during the registration process. The approach is evaluated for the registration of lungs based on 22 thoracic 4D CT data sets. Experiments show that the TRE can be reduced on average by 0.07 mm using automatic parameter optimization.

Citation Download Citation

Alexander Schmidt-Richberg, René Werner, Jan Ehrhardt, Jan-Christoph Wolf, and Heinz Handels "Landmark-driven parameter optimization for non-linear image registration", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79620T (11 March 2011); https://doi.org/10.1117/12.877059

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

Includes PDF, HTML & Video, when available