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Model-based computed tomography image estimation: partitioning approach
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0001-5673-620x
2019 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 46, no 14, p. 2627-2648Article in journal (Refereed) Published
Abstract [en]

There is a growing interest to get a fully MR based radiotherapy. The most important development needed is to obtain improved bone tissue estimation. The existing model-based methods perform poorly on bone tissues. This paper was aimed at obtaining improved bone tissue estimation. Skew-Gaussian mixture model and Gaussian mixture model were proposed to investigate CT image estimation from MR images by partitioning the data into two major tissue types. The performance of the proposed models was evaluated using the leaveone-out cross-validation method on real data. In comparison with the existing model-based approaches, the model-based partitioning approach outperformed in bone tissue estimation, especially in dense bone tissue estimation.

Place, publisher, year, edition, pages
Taylor & Francis, 2019. Vol. 46, no 14, p. 2627-2648
Keywords [en]
Computed tomography, magnetic resonance imaging, CT image estimation, skew-Gaussian mixture model, Gaussian mixture model
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-158259DOI: 10.1080/02664763.2019.1606169ISI: 000465945500001OAI: oai:DiVA.org:umu-158259DiVA, id: diva2:1305629
Available from: 2019-04-17 Created: 2019-04-17 Last updated: 2019-08-29Bibliographically approved

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Bayisa, FekaduYu, Jun
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