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A Gaussian Mixture Model based Level Set Method for Volume Segmentation in Medical Images
Linköping University, Department of Mathematics, Computational Mathematics. Linköping University, Faculty of Science & Engineering.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis proposes a probabilistic level set method to be used in segmentation of tumors with heterogeneous intensities. It models the intensities of the tumor and surrounding tissue using Gaussian mixture models. Through a contour based initialization procedure samples are gathered to be used in expectation maximization of the mixture model parameters. The proposed method is compared against a threshold-based segmentation method using MRI images retrieved from The Cancer Imaging Archive. The cases are manually segmented and an automated testing procedure is used to find optimal parameters for the proposed method and then it is tested against the threshold-based method. Segmentation times, dice coefficients, and volume errors are compared. The evaluation reveals that the proposed method has a comparable mean segmentation time to the threshold-based method, and performs faster in cases where the volume error does not exceed 40%. The mean dice coefficient and volume error are also improved while achieving lower deviation.

Place, publisher, year, edition, pages
2018. , p. 63
Keywords [en]
Probablistic level set methods, Gaussian mixture models, image segmentation, volume segmentation, medical images
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-148548ISRN: LiTH-MAT-EX--2018/08--SEOAI: oai:DiVA.org:liu-148548DiVA, id: diva2:1217463
External cooperation
Sectra
Subject / course
Mathematics
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Available from: 2018-06-14 Created: 2018-06-13 Last updated: 2018-06-14Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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  • de-DE
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  • en-US
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  • Other locale
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Output format
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