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Model-based computed tomography image estimation: partitioning approach
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.ORCID-id: 0000-0001-5673-620x
2019 (engelsk)Inngår i: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 46, nr 14, s. 2627-2648Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Taylor & Francis, 2019. Vol. 46, nr 14, s. 2627-2648
Emneord [en]
Computed tomography, magnetic resonance imaging, CT image estimation, skew-Gaussian mixture model, Gaussian mixture model
HSV kategori
Forskningsprogram
matematisk statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-158259DOI: 10.1080/02664763.2019.1606169ISI: 000465945500001OAI: oai:DiVA.org:umu-158259DiVA, id: diva2:1305629
Tilgjengelig fra: 2019-04-17 Laget: 2019-04-17 Sist oppdatert: 2019-08-29bibliografisk kontrollert

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