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Cited by 44Advanced Radiotherapy TechniquesMedical Image Segmentation TechniquesRadiation Dose and Imaging

Stochastic rank correlation: A robust merit function for 2D/3D registration of image data obtained at different energies

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Birkfellner, Wolfgang, Stock, Markus, Figl, Michael, Gendrin, Christelle, Hummel, Johann, Dong, Shuo, Kettenbach, Joachim, Georg, Dietmar +1 more·2009·Medical Physics
DOI 10.1118/1.3157111PMC PMC2855948
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Abstract

In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.

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  • Comparative studies suggest that local metrics tend to demonstrate better performance than global metrics.

    “Although the best performing similarity metric is application-dependent, comparative studies (Birkfellner et al 2009, Russakoff et al 2003) suggest that local metrics tend to demonstrate better performance.”

    From Robust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation (2013) · cites this paper as Birkfellner et al 2009

    The Introduction lists local normalized correlation among registration metrics, but does not compare local metrics with global ones. The Results compare specific methods—SRC, cross correlation, and pattern intensity—and report that SRC performed best in the tested setup. Those findings do not establish the claimed general tendency for local metrics to perform better. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Birkfellner, W., Stock, M., Figl, M., Gendrin, C., Hummel, J., Dong, S., Kettenbach, J., Georg, D., & Bergmann, H.
Affiliations
Medical University of Vienna (AT)
Corresponding author
Birkfellner, Wolfgang
ORCID
Venue
Medical Physics
Publisher
Wiley
Published
2009-07-01
Volume
36
Issue
8
Pages
3420-3428
ISSN
0094-2405 (ISSN-L)
DOI
10.1118/1.3157111
PMC
PMC2855948
OpenAlex
W2035539344
Cited by
44
References
0
Citations by year
2024: 2 · 2023: 1 · 2022: 4 · 2021: 2 · 2020: 2 · 2019: 3 · 2018: 2 · 2017: 2 · 2016: 4 · 2015: 1 · 2014: 4 · 2013: 2 · 2012: 4
FWCI
4.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Medical Physics (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • BORIS (University Library Bern) (submittedVersion)
Funders
  • Eurasia-Pacific Uninet Foundation (P19931)
  • Austrian Science Foundation FWF (L503)
Topics
  • Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
Keywords
2D-3D registration, merit function, image-guided radiotherapy, intensity invariance, Spearman correlation, cross correlation function, mutual information, radiometric calibration
MeSH terms
Cadaver, Humans, Reference Standards, Spine, Stochastic Processes, Time Factors, Tomography, X-Ray Computed, Phantoms, Imaging, Imaging, Three-Dimensional

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