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Segmentation of motor units in ultrasound image sequences
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The archetypal modern comic book superhero, Superman, has two superpowers of interest: the ability to see into objects and the ability to see distant objects. Now, humans possess these powers as well, due to the medical ultrasound imaging and sound navigation. Ultrasound, a type of sound we cannot hear, has enabled us to see a world otherwise invisible to us.

Ultrasound medical imaging can be used to visualize and quantify anatomical and functional aspects of internal tissues and organs of the human body. Skeletal muscle tissue is functionally composed by so called motor units which are the smallest voluntarily activatable units and is of primary interest in this study.

The major complexity in segmentation of motor units in skeletal muscle tissue in ultrasound image sequences is the aspect of overlapping objects. We propose a framework and evaluate the performance on simulated synthetic data.

We have found that it is possible to segment motor units under an isometric contraction using high-end ultrasound scanners and we have proposed a framework which is robust when simulating up to 10 components when exposed to 20 dB Gaussian white noise. The framework is not satisfactory robust when exposed to significant amount of noise. In order to be able to segment a large number of components, decomposition is inevitable and together with development of a step including smoothing, the framework can be further improved. 

Place, publisher, year, edition, pages
2016.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-126896OAI: oai:DiVA.org:umu-126896DiVA: diva2:1038693
Supervisors
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Available from: 2016-12-05 Created: 2016-10-19 Last updated: 2016-12-05Bibliographically approved

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Rohlén, Robin
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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