Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Elekta Instrument AB, Stockholm, Sweden.
Elekta Instrument AB, Stockholm, Sweden.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
Show others and affiliations
2014 (English)In: 22nd International Conference on Pattern Recognition (ICPR), 2014, IEEE , 2014, 3274-3279 p.Conference paper, Published paper (Refereed)
Abstract [en]

Magnetic resonance (MR) images lack information about radiation transport-a fact which is problematic in applications such as radiotherapy planning and attenuation correction in combined PET/MR imaging. To remedy this, a crude but common approach is to approximate all tissue properties as equivalent to those of water. We improve upon this using an algorithm that automatically identifies bone tissue in MR. More specifically, we focus on segmenting the skull prior to stereotactic neurosurgery, where it is common that only MR images are available. In the proposed approach, a machine learning algorithm known as a support vector machine is trained on patients for which both a CT and an MR scan are available. As input, a combination of local and global information is used. The latter is needed to distinguish between bone and air as this is not possible based only on the local image intensity. A whole skull segmentation is achievable in minutes. In a comparison with two other methods, one based on mathematical morphology and the other on deformable registration, the proposed method was found to yield consistently better segmentations.

Place, publisher, year, edition, pages
IEEE , 2014. 3274-3279 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
Keyword [en]
Bones; Computed tomography; Image segmentation; Magnetic resonance imaging; Positron emission tomography; Support vector machines; Training
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:liu:diva-113296DOI: 10.1109/ICPR.2014.564ISI: 000359818003068OAI: oai:DiVA.org:liu-113296DiVA: diva2:780820
Conference
22nd International Conference on Pattern Recognition (ICPR), 2014, 24-28 August, Stockholm, Sweden
Available from: 2015-01-15 Created: 2015-01-15 Last updated: 2015-09-14Bibliographically approved
In thesis
1. MRI based radiotherapy planning and pulse sequence optimization
Open this publication in new window or tab >>MRI based radiotherapy planning and pulse sequence optimization
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential.

Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning algorithm to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs.

Cancerous tissue has a dierent structure from normal tissue. This affects molecular diusion, which can be measured using MRI. The prototypical diusion encoding sequence has recently been challenged with the introduction of more general 

waveforms. To take full advantage of their capabilities it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. 45 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1713
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-115796 (URN)10.3384/lic.diva-115796 (DOI)978-91-7519-105-8 (ISBN)
Presentation
2015-04-13, IMT, Campus US, Linköpings universitet, Linköping, 13:15 (Swedish)
Opponent
Supervisors
Funder
Swedish Research Council
Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2015-03-20Bibliographically approved

Open Access in DiVA

fulltext(2449 kB)417 downloads
File information
File name FULLTEXT01.pdfFile size 2449 kBChecksum SHA-512
d774158d3902e4467083efa5715444636bac86f764143e4ad064682ec5dfcb4388137a430d605cd0e30bc9b094b4bc54c0b13a53525a00a0b7e9232158020f3d
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Sjölund, JensEriksson Jarliden, AndreasAndersson, MatsKnutsson, Hans
By organisation
Medical InformaticsThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)Faculty of Science & Engineering
Physical Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 417 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
urn-nbn

Altmetric score

doi
urn-nbn
Total: 302 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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