| Automatic parameter selection for multimodal image registration. | |
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MedLine Citation:
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PMID: 20236877 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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Over the past ten years similarity measures based on intensity distributions have become state-of-the-art in automatic multimodal image registration. An implementation for clinical usage has to support a plurality of images. However, a generally applicable parameter configuration for the number and sizes of histogram bins, optimal Parzen-window kernel widths or background thresholds cannot be found. This explains why various research groups present partly contradictory empirical proposals for these parameters. This paper proposes a set of data-driven estimation schemes for a parameter-free implementation that eliminates major caveats of heuristic trial and error. We present the following novel approaches: a new coincidence weighting scheme to reduce the influence of background noise on the similarity measure in combination with Max-Lloyd requantization, and a tradeoff for the automatic estimation of the number of histogram bins. These methods have been integrated into a state-of-the-art rigid registration that is based on normalized mutual information and applied to CT-MR, PET-MR, and MR-MR image pairs of the RIRE 2.0 database. We compare combinations of the proposed techniques to a standard implementation using default parameters, which can be found in the literature, and to a manual registration by a medical expert. Additionally, we analyze the effects of various histogram sizes, sampling rates, and error thresholds for the number of histogram bins. The comparison of the parameter selection techniques yields 25 approaches in total, with 114 registrations each. The number of bins has no significant influence on the proposed implementation that performs better than both the manual and the standard method in terms of acceptance rates and target registration error (TRE). The overall mean TRE is 2.34 mm compared to 2.54 mm for the manual registration and 6.48 mm for a standard implementation. Our results show a significant TRE reduction for distortion-corrected magnetic resonance images. |
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Authors:
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Dieter A Hahn; Volker Daum; Joachim Hornegger |
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Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't Date: 2010-03-15 |
Journal Detail:
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Title: IEEE transactions on medical imaging Volume: 29 ISSN: 1558-0062 ISO Abbreviation: IEEE Trans Med Imaging Publication Date: 2010 May |
Date Detail:
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Created Date: 2010-07-27 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8310780 Medline TA: IEEE Trans Med Imaging Country: United States |
Other Details:
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Languages: eng Pagination: 1140-55 Citation Subset: IM |
Affiliation:
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Friedrich-Alexander-University of Erlangen-Nuremberg (FAU), Pattern Recognition Lab, Department of Computer Science, 91058 Erlangen, Germany. dieter.hahn@informatik.uni-erlangen.de |
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Descriptor/Qualifier:
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| Grant Support | |
ID/Acronym/Agency:
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1 R01 NS33926-02/NS/NINDS NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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