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Blood vessel segmentation using multi-scale quadrature filtering
Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6457-4914
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
2010 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 31, no 8, 762-767 p.Article in journal (Refereed) Published
Abstract [en]

The segmentation of blood vessels is a common problem in medical imagingand various applications are found in diagnostics, surgical planning, trainingand more. Among many dierent techniques, the use of multiple scales andline detectors is a popular approach. However, the typical line lters usedare sensitive to intensity variations and do not target the detection of vesselwalls explicitly. In this article, we combine both line and edge detection usingquadrature lters across multiple scales. The lter result gives well denedvessels as linear structures, while distinct edges facilitate a robust segmentation.We apply the lter output to energy optimization techniques for segmentationand show promising results in 2D and 3D to illustrate the behavior of ourmethod. The conference version of this article received the best paper award inthe bioinformatics and biomedical applications track at ICPR 2008.

Place, publisher, year, edition, pages
Elsevier , 2010. Vol. 31, no 8, 762-767 p.
Keyword [en]
Image segmentation, Blood vessels, Medical imaging, Multi-scale, Quadrature filter, Level set method
National Category
Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:liu:diva-21046DOI: 10.1016/j.patrec.2009.09.020ISI: 000277552600014OAI: oai:DiVA.org:liu-21046DiVA: diva2:240466
Note
Original Publication: Gunnar Läthén, Jimmy Jonasson and Magnus Borga, Blood vessel segmentation using multi-scale quadrature filtering, 2010, Pattern Recognition Letters, (31), 8, 762-767. http://dx.doi.org/10.1016/j.patrec.2009.09.020 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/ Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2017-12-13
In thesis
1. Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods
Open this publication in new window or tab >>Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods
2010 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Image segmentation is the problem of partitioning an image into meaningful parts, often consisting of an object and background. As an important part of many imaging applications, e.g. face recognition, tracking of moving cars and people etc, it is of general interest to design robust and fast segmentation algorithms. However, it is well accepted that there is no general method for solving all segmentation problems. Instead, the algorithms have to be highly adapted to the application in order to achieve good performance. In this thesis, we will study segmentation methods for blood vessels in medical images. The need for accurate segmentation tools in medical applications is driven by the increased capacity of the imaging devices. Common modalities such as CT and MRI generate images which simply cannot be examined manually, due to high resolutions and a large number of image slices. Furthermore, it is very difficult to visualize complex structures in three-dimensional image volumes without cutting away large portions of, perhaps important, data. Tools, such as segmentation, can aid the medical staff in browsing through such large images by highlighting objects of particular importance. In addition, segmentation in particular can output models of organs, tumors, and other structures for further analysis, quantification or simulation.

We have divided the segmentation of blood vessels into two parts. First, we model the vessels as a collection of lines and edges (linear structures) and use filtering techniques to detect such structures in an image. Second, the output from this filtering is used as input for segmentation tools. Our contributions mainly lie in the design of a multi-scale filtering and integration scheme for de- tecting vessels of varying widths and the modification of optimization schemes for finding better segmentations than traditional methods do. We validate our ideas on synthetical images mimicking typical blood vessel structures, and show proof-of-concept results on real medical images.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 44 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1434
Keyword
Image segmentation, Medical image analysis, Level set method, Quadrature filter, Multi-scale
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-54181 (URN)LIU-TEK-LIC-2010:5 (Local ID)978-91-7393-410-7 (ISBN)LIU-TEK-LIC-2010:5 (Archive number)LIU-TEK-LIC-2010:5 (OAI)
Presentation
2010-04-15, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 13:00 (English)
Opponent
Supervisors
Available from: 2010-04-20 Created: 2010-03-01 Last updated: 2016-08-31Bibliographically approved
2. 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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