Metric-Based Pairwise and Multiple Image Registration

Qian Xie; Sebastian Kurtek; Eric Klassen; Gary E Christensen; Anuj Srivastava

doi:10.1007/978-3-319-10605-2_16

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Metric-Based Pairwise and Multiple Image Registration

Book chapter

Open access

Peer reviewed

Metric-Based Pairwise and Multiple Image Registration

Qian Xie, Sebastian Kurtek, Eric Klassen, Gary E Christensen and Anuj Srivastava

Computer Vision – ECCV 2014, pp.236-250

Lecture Notes in Computer Science, Springer International Publishing

2014

DOI: 10.1007/978-3-319-10605-2_16

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Abstract

Registering pairs or groups of images is a widely-studied problem that has seen a variety of solutions in recent years. Most of these solutions are variational, using objective functions that should satisfy several basic and desired properties. In this paper, we pursue two additional properties – (1) invariance of objective function under identical warping of input images and (2) the objective function induces a proper metric on the set of equivalence classes of images – and motivate their importance. Then, a registration framework that satisfies these properties, using the L2-norm between a novel representation of images, is introduced. Additionally, for multiple images, the induced metric enables us to compute a mean image, or a template, and perform joint registration. We demonstrate this framework using examples from a variety of image types and compare performances with some recent methods.

elastic image deformation

mean image

metric-based registration

multiple registration

post-registration analysis

Details

Title: Subtitle: Metric-Based Pairwise and Multiple Image Registration
Creators: Qian Xie - Florida State University
Sebastian Kurtek - Ohio State University
Eric Klassen - Florida State University
Gary E Christensen - University of Iowa
Anuj Srivastava - Florida State University
Resource Type: Book chapter
Publication Details: Computer Vision – ECCV 2014, pp.236-250
Series: Lecture Notes in Computer Science
DOI: 10.1007/978-3-319-10605-2_16
eISSN: 1611-3349
ISSN: 0302-9743
Publisher: Springer International Publishing; Cham
Language: English
Date published: 2014
Academic Unit: Electrical and Computer Engineering; Radiation Oncology; Radiation Research Laboratory; The Iowa Institute for Biomedical Imaging; Advanced Pulmonary Physiomic Imaging Laboratory; Holden Comprehensive Cancer Center
Record Identifier: 9984197269702771

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