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Phase Based Level Set Segmentation of Blood Vessels
Linköpings universitet, Institutionen för teknik och naturvetenskap, Digitala Medier. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-6457-4914
Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-9267-2191
2008 (engelsk)Inngår i: Proceedings of 19th International Conference on Pattern Recognition, IEEE Computer Society , 2008, s. 1-4Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The segmentation and analysis of blood vessels hasreceived much attention in the research community. Theresults aid numerous applications for diagnosis andtreatment of vascular diseases. Here we use level setpropagation with local phase information to capture theboundaries of vessels. The basic notion is that localphase, extracted using quadrature filters, allows us todistinguish between lines and edges in an image. Notingthat vessels appear either as lines or edge pairs, weintegrate multiple scales and capture information aboutvessels of varying width. The outcome is a “global”phase which can be used to drive a contour robustly towardsthe vessel edges. We show promising results in2D and 3D. Comparison with a related method givessimilar or even better results and at a computationalcost several orders of magnitude less. Even with verysparse initializations, our method captures a large portionof the vessel tree.

sted, utgiver, år, opplag, sider
IEEE Computer Society , 2008. s. 1-4
Serie
International Conference on Pattern Recognition, ISSN 1051-4651
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-21054DOI: 10.1109/ICPR.2008.4760970ISI: 000264729000023ISBN: 978-1-4244-2175-6 (tryckt)ISBN: 978-1-4244-2174-9 (tryckt)OAI: oai:DiVA.org:liu-21054DiVA, id: diva2:240478
Konferanse
19th International Conference on Pattern Recognition (ICPR 2008), 8-11 December 2008, Tampa, Finland
Merknad

©2009 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, Jimmy Jonasson and Magnus Borga, Phase Based Level Set Segmentation of Blood Vessels, 2008, Proceedings of 19th International Conference on Pattern Recognition. http://dx.doi.org/10.1109/ICPR.2008.4760970

Tilgjengelig fra: 2009-09-28 Laget: 2009-09-28 Sist oppdatert: 2015-10-09
Inngår i avhandling
1. Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods
Åpne denne publikasjonen i ny fane eller vindu >>Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods
2010 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2010. s. 44
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1434
Emneord
Image segmentation, Medical image analysis, Level set method, Quadrature filter, Multi-scale
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-54181 (URN)LIU-TEK-LIC-2010:5 (Lokal ID)978-91-7393-410-7 (ISBN)LIU-TEK-LIC-2010:5 (Arkivnummer)LIU-TEK-LIC-2010:5 (OAI)
Presentation
2010-04-15, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2010-04-20 Laget: 2010-03-01 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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