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Coverage segmentation of 3D thin structures
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.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
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2015 (English)In: Proc. 5th International Conference on Image Processing Theory, Tools and Applications, Piscataway, NJ: IEEE , 2015, 23-28 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE , 2015. 23-28 p.
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-267161DOI: 10.1109/IPTA.2015.7367089ISI: 000380472700002ISBN: 978-1-4799-8636-1 (print)OAI: oai:DiVA.org:uu-267161DiVA: diva2:872420
Conference
IPTA 2015, November 10–13, Orléans, France
Funder
Swedish Research Council, 621-2014-6153
Available from: 2016-01-12 Created: 2015-11-18 Last updated: 2017-03-29Bibliographically approved
In thesis
1. Fast Methods for Vascular Segmentation Based on Approximate Skeleton Detection
Open this publication in new window or tab >>Fast Methods for Vascular Segmentation Based on Approximate Skeleton Detection
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modern medical imaging techniques have revolutionized health care over the last decades, providing clinicians with high-resolution 3D images of the inside of the patient's body without the need for invasive procedures. Detailed images of the vascular anatomy can be captured by angiography, providing a valuable source of information when deciding whether a vascular intervention is needed, for planning treatment, and for analyzing the success of therapy. However, increasing level of detail in the images, together with a wide availability of imaging devices, lead to an urgent need for automated techniques for image segmentation and analysis in order to assist the clinicians in performing a fast and accurate examination.

To reduce the need for user interaction and increase the speed of vascular segmentation,  we propose a fast and fully automatic vascular skeleton extraction algorithm. This algorithm first analyzes the volume's intensity histogram in order to automatically adapt the internal parameters to each patient and then it produces an approximate skeleton of the patient's vasculature. The skeleton can serve as a seed region for subsequent surface extraction algorithms. Further improvements of the skeleton extraction algorithm include the expansion to detect the skeleton of diseased arteries and the design of a convolutional neural network classifier that reduces false positive detections of vascular cross-sections. In addition to the complete skeleton extraction algorithm, the thesis presents a segmentation algorithm based on modified onion-kernel region growing. It initiates the growing from the previously extracted skeleton and provides a rapid binary segmentation of tubular structures. To provide the possibility of extracting precise measurements from this segmentation we introduce a method for obtaining a segmentation with subpixel precision out of the binary segmentation and the original image. This method is especially suited for thin and elongated structures, such as vessels, since it does not shrink the long protrusions. The method supports both 2D and 3D image data.

The methods were validated on real computed tomography datasets and are primarily intended for applications in vascular segmentation, however, they are robust enough to work with other anatomical tree structures after adequate parameter adjustment, which was demonstrated on an airway-tree segmentation.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 79 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1496
Keyword
medical image analysis, automatic skeleton extraction, vascular segmentation, coverage segmentation, convolutional neural network classifier, CT angiography
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-318796 (URN)978-91-554-9874-0 (ISBN)
Public defence
2017-05-22, ITC 2446, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Funder
Swedish Research Council, grant no. 621-2014-6153
Available from: 2017-04-27 Created: 2017-03-29 Last updated: 2017-05-05

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Lidayová, KristínaLindblad, JoakimSladoje, NatašaFrimmel, Hans

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Lidayová, KristínaLindblad, JoakimSladoje, NatašaFrimmel, Hans
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Medical Image Processing

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