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Non-ring Filters for Robust Detection of Linear Structures
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). (Computer Graphics and Image Processing)ORCID iD: 0000-0002-6457-4914
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9267-2191
2010 (English)In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, 233-236 p.Conference paper, Published paper (Refereed)
Abstract [en]

Many applications in image analysis include the problem of linear structure detection, e.g. segmentation of blood vessels in medical images, roads in satellite images, etc. A simple and efficient solution is to apply linear filters tuned to the structures of interest and extract line and edge positions from the filter output. However, if the filter is not carefully designed, artifacts such as ringing can distort the results and hinder a robust detection. In this paper, we study the ringing effects using a common Gabor filter for linear structure detection, and suggest a method for generating non-ring filters in 2D and 3D. The benefits of the non-ring design are motivated by results on both synthetic and natural images.

Place, publisher, year, edition, pages
Los Alamitos, CA, USA: IEEE Computer Society, 2010. 233-236 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
Keyword [en]
ringing filters, Gabor, non-ring filters, edge detection, filter design
National Category
Engineering and Technology Computer and Information Science Computer Vision and Robotics (Autonomous Systems) Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-58850DOI: 10.1109/ICPR.2010.66ISBN: 978-1-4244-7542-1 (print)OAI: oai:DiVA.org:liu-58850DiVA: diva2:346058
Conference
20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010
Note

©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Gunnar Läthén, Olivier Cros, Hans Knutsson and Magnus Borga, Non-ring Filters for Robust Detection of Linear Structures, 2010, Proceedings of the 20th International Conference on Pattern Recognition, 233-236. http://dx.doi.org/10.1109/ICPR.2010.66

Available from: 2010-08-30 Created: 2010-08-30 Last updated: 2014-10-08Bibliographically approved
In thesis
1. Level Set Segmentation and Volume Visualization of Vascular Trees
Open this publication in new window or tab >>Level Set Segmentation and Volume Visualization of Vascular Trees
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Medical imaging is an important part of the clinical workflow. With the increasing amount and complexity of image data comes the need for automatic (or semi-automatic) analysis methods which aid the physician in the exploration of the data. One specific imaging technique is angiography, in which the blood vessels are imaged using an injected contrast agent which increases the contrast between blood and surrounding tissue. In these images, the blood vessels can be viewed as tubular structures with varying diameters. Deviations from this structure are signs of disease, such as stenoses introducing reduced blood flow, or aneurysms with a risk of rupture. This thesis focuses on segmentation and visualization of blood vessels, consituting the vascular tree, in angiography images.

Segmentation is the problem of partitioning an image into separate regions. There is no general segmentation method which achieves good results for all possible applications. Instead, algorithms use prior knowledge and data models adapted to the problem at hand for good performance. We study blood vessel segmentation based on a two-step approach. First, we model the vessels as a collection of linear structures which are detected using multi-scale filtering techniques. Second, we develop machine-learning based level set segmentation methods to separate the vessels from the background, based on the output of the filtering.

In many applications the three-dimensional structure of the vascular tree has to be presented to a radiologist or a member of the medical staff. For this, a visualization technique such as direct volume rendering is often used. In the case of computed tomography angiography one has to take into account that the image depends on both the geometrical structure of the vascular tree and the varying concentration of the injected contrast agent. The visualization should have an easy to understand interpretation for the user, to make diagnostical interpretations reliable. The mapping from the image data to the visualization should therefore closely follow routines that are commonly used by the radiologist. We developed an automatic method which adapts the visualization locally to the contrast agent, revealing a larger portion of the vascular tree while minimizing the manual intervention required from the radiologist. The effectiveness of this method is evaluated in a user study involving radiologists as domain experts.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 86 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1543
Keyword
level set methods, image segmentation, edge detection, visualization, volume rendering, blood vessels, angiography, vascular trees
National Category
Media and Communication Technology Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-97371 (URN)978-91-7519-514-8 (ISBN)
Public defence
2013-10-21, Domteatern, Visualiseringscenter C, Kungsgatan 54, Norrköping, 14:00 (English)
Opponent
Supervisors
Available from: 2013-09-11 Created: 2013-09-10 Last updated: 2016-08-31Bibliographically approved

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Läthén, GunnarCros, OlivierKnutsson, HansBorga, Magnus

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