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Enhancement of cilia sub-structures by multiple instance registration and super-resolution reconstruction
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.
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
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: 0000-0001-7312-8222
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.ORCID iD: 0000-0003-2777-8114
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2017 (English)In: Image Analysis: Part II, Springer, 2017, p. 362-374Conference paper, Published paper (Refereed)
Abstract [en]

Ultrastructural analysis of cilia cross-sectional images using transmission electron microscopy (TEM) assists the pathologists to diagnose Primary Ciliary Dyskinesia, a genetic disease. The current diagnostic procedure is manual and difficult because of poor signal-to-noise ratio in TEM images. In this paper, we propose an automated multi-step registration approach to register many cilia cross-sectional instances. The novelty of the work is in the utilization of customized weight masks at each registration step to achieve good alignment of the specific cilium regions. Registration is followed by super-resolution reconstruction to enhance the substructural information. Landmarks matching based evaluation of registration results in pixel alignment error of 2.35±1.82" role="presentation">2.35±1.82 pixels, and the subjective analysis of super-resolution reconstructed cilium shows a clear improvement in the visibility of the substructures such as dynein arms, radial spokes, and central pair.

Place, publisher, year, edition, pages
Springer, 2017. p. 362-374
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10270
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-334225DOI: 10.1007/978-3-319-59129-2_31ISI: 000454360300031ISBN: 978-3-319-59128-5 (print)OAI: oai:DiVA.org:uu-334225DiVA, id: diva2:1159093
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Available from: 2017-05-19 Created: 2017-11-21 Last updated: 2019-04-17Bibliographically approved
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Suveer, AmitLindblad, JoakimDragomir, AncaSintorn, Ida-Maria

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Suveer, AmitSladoje, NatašaLindblad, JoakimDragomir, AncaSintorn, Ida-Maria
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Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionClinical and experimental pathology
Medical Image Processing

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