Paper Info
Title
Landmark-driven parameter optimization for non-linear image registration
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.
Year
DOI
Venue
2011
10.1117/12.877059
Proceedings of SPIE
Keywords
Field
DocType
registration,landmark detection,parameter optimization
Computer vision,Data set,Nonlinear system,Image fusion,Pattern recognition,Computer science,Image processing,Artificial intelligence,Motion estimation,Landmark,Image registration
Conference
Volume
ISSN
Citations 
7962
0277-786X
3
PageRank 
References 
Authors
0.66
5
5
Name
Order
Citations
PageRank
Alexander Schmidt-Richberg122624.43
René Werner25714.22
Jan Ehrhardt338754.33
Jan-Christoph Wolf441.87
Heinz Handels51527239.84