liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Momentum Based Optimization Methods for Level Set Segmentation
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, 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). Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-7557-4904
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
2009 (English)In: Momentum Based Optimization Methods for Level Set Segmentation: Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings / [ed] Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen, Berlin: Springer Berlin/Heidelberg, 2009, 124-136 p.Conference paper, Published paper (Refereed)
Abstract [en]

Segmentation of images is often posed as a variational problem. As such, it is solved by formulating an energy functional depending on a contour and other image derived terms. The solution of the segmentation problem is the contour which extremizes this functional. The standard way of solving this optimization problem is by gradient descent search in the solution space, which typically suffers from many unwanted local optima and poor convergence. Classically, these problems have been circumvented by modifying the energy functional. In contrast, the focus of this paper is on alternative methods for optimization. Inspired by ideas from the machine learning community, we propose segmentation based on gradient descent with momentum. Our results show that typical models hampered by local optima solutions can be further improved by this approach. We illustrate the performance improvements using the level set framework.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2009. 124-136 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 5567
National Category
Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:liu:diva-21037DOI: 10.1007/978-3-642-02256-2_11ISI: 000270543900011ISBN: 3-642-02255-3 (print)ISBN: 978-3-642-02255-5 (print)ISBN: e-978-3-642-02256-2 OAI: oai:DiVA.org:liu-21037DiVA: diva2:240452
Conference
Second International Conference, SSVM 2009, June 1-5, Voss, Norway
Note

Original Publication: Gunnar Läthén, Thord Andersson, Reiner Lenz and Magnus Borga, Momentum Based Optimization Methods for Level Set Segmentation, 2009, Lecture Notes in Computer Science 5567: Scale Space and Variational Methods in Computer Vision, 124-136. http://dx.doi.org/10.1007/978-3-642-02256-2_11 Copyright: Springer http://www.springerlink.com/

Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2016-08-31Bibliographically approved
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

Open Access in DiVA

fulltext(1358 kB)1802 downloads
File information
File name FULLTEXT02.pdfFile size 1358 kBChecksum SHA-512
a8af1996f28ca0cd43cd21a258417284dc799be542d715d3910bd8af0531e632dc6de5f5977df0642b688eacc618403000e49e7cc126fc75c64714abe2c8d258
Type fulltextMimetype application/pdf

Other links

Publisher's full textfind book at a swedish library/hitta boken i ett svenskt bibliotek

Authority records BETA

Läthén, GunnarAndersson, ThordLenz, ReinerBorga, Magnus

Search in DiVA

By author/editor
Läthén, GunnarAndersson, ThordLenz, ReinerBorga, Magnus
By organisation
Center for Medical Image Science and Visualization (CMIV)Digital MediaThe Institute of TechnologyMedical Informatics
Medical Laboratory and Measurements Technologies

Search outside of DiVA

GoogleGoogle Scholar
Total: 1802 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 978 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf