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Automatic Segmentation of Knee Cartilage Using Quantitative MRI Data
Linköping University, Department of Electrical Engineering, Computer Vision.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis investigates if support vector machine classification is a suitable approach when performing automatic segmentation of knee cartilage using quantitative magnetic resonance imaging data. The data sets used are part of a clinical project that investigates if patients that have suffered recent knee damage will develop cartilage damage. Therefore the thesis also investigates if the segmentation results can be used to predict the clinical outcome of the patients.

Two methods that perform the segmentation using support vector machine classification are implemented and evaluated. The evaluation indicates that it is a good approach for the task, but the implemented methods needs to be further improved and tested on more data sets before clinical use.

It was not possible to relate the cartilage properties to clinical outcome using the segmentation results. However, the investigation demonstrated good promise of how the segmentation results, if they are improved, can be used in combination with quantitative magnetic resonance imaging data to analyze how the cartilage properties change over time or vary between knees.

Place, publisher, year, edition, pages
2017. , p. 46
Keywords [en]
qMRI, quantitative MRI, automatic segmentation, knee cartilage, SVM classifier
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-138403ISRN: LiTH-ISY-EX--17/5041--SEOAI: oai:DiVA.org:liu-138403DiVA, id: diva2:1109911
External cooperation
SyntheticMR AB
Subject / course
Computer Vision Laboratory
Examiners
Available from: 2017-06-15 Created: 2017-06-14 Last updated: 2017-06-15Bibliographically approved

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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