Book chapter
A Novel Framework for Metric-Based Image Registration
Biomedical Image Registration, pp.276-285
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2012
DOI: 10.1007/978-3-642-31340-0_29
Abstract
The registrations of functions and images is a widely-studied problem that has seen a variety of solutions in the recent years. Most of these solutions are based on objective functions that fail to satisfy two most basic and desired properties in registration: (1) invariance under identical warping: since the registration between two images is unchanged under identical domain warping, the cost function evaluating registrations should also remain unchanged; (2) inverse consistency: the optimal registration of image A to B should be the same as that of image B to A. We present a novel registration approach that uses the L2 norm, between certain vector fields derived from images, as an objective function for registering images. This framework satisfies symmetry and invariance properties. We demonstrate this framework using examples from different types of images and compare performances with some recent methods.
Details
- Title: Subtitle
- A Novel Framework for Metric-Based Image Registration
- Creators
- Qian Xie - Florida State UniversitySebastian Kurtek - Florida State UniversityGary E Christensen - University of IowaZhaohua Ding - Vanderbilt UniversityEric Klassen - Florida State UniversityAnuj Srivastava - Florida State University
- Resource Type
- Book chapter
- Publication Details
- Biomedical Image Registration, pp.276-285
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-642-31340-0_29
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2012
- Academic Unit
- Radiation Research Laboratory; The Iowa Institute for Biomedical Imaging; Advanced Pulmonary Physiomic Imaging Laboratory; Holden Comprehensive Cancer Center; Electrical and Computer Engineering; Radiation Oncology
- Record Identifier
- 9984197299702771
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