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Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0003-0253-9037
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.ORCID iD: 0000-0001-7312-8222
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0002-6041-6310
2019 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042Article in journal (Refereed) Epub ahead of print
Abstract [en]

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradientbased registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.

Place, publisher, year, edition, pages
IEEE, 2019.
Keywords [en]
Image registration, set distance, gradient methods, optimization, cost function, iterative algorithms, fuzzy sets, magnetic resonance imaging, transmission electron microscopy
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-377450DOI: 10.1109/TIP.2019.2899947OAI: oai:DiVA.org:uu-377450DiVA, id: diva2:1290252
Available from: 2019-02-20 Created: 2019-02-20 Last updated: 2019-03-03

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Öfverstedt, JohanLindblad, JoakimSladoje, Natasa

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Medical Image Processing

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