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Kidney Dynamic Model Enrichment
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis explores and explains a method using discrete curvature as a feature to find regions of vertices that can be classified as being likely to indicate the presence of an underlying tumor on a kidney surface mesh. Vertices are tagged based on curvature type and mathematical morphology is used to form regions on the mesh. The size and location of the tumor is approximated by fitting a sphere to this region. The method is intended to be employed in noninvasive radiotherapy with a dynamic soft tissue model. It could also provide an alternative to volumetric methods used to segment tumors. A validation is made using the images from which the kidney mesh was constructed, the tumor is visible as a comparison to the method result.

The dynamic kidney model is validated using the Hausdorff distance and it is explained how this can be computed in an effective way using bounding volume hierarchies.

Both the tumor finding method and the dynamic model show promising results since they lie within the limit used by practitioners during therapy.

Place, publisher, year, edition, pages
2015. , 40 p.
UPTEC F, ISSN 1401-5757 ; 15003
Keyword [en]
discrete curvature, 3d programming, best fit sphere, mesh processing, kidney, tumor, radiotherapy
National Category
Medical Image Processing Computational Mathematics Other Computer and Information Science
URN: urn:nbn:se:uu:diva-242315OAI: diva2:783147
Educational program
Master Programme in Engineering Physics
Available from: 2015-01-28 Created: 2015-01-23 Last updated: 2015-01-28Bibliographically approved

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Medical Image ProcessingComputational MathematicsOther Computer and Information Science

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