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Model-based computed tomography image estimation: partitioning approach

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  • Fekadu L. Bayisa
  • Jun Yu

Abstract

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 leave-one-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.

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  • Fekadu L. Bayisa & Jun Yu, 2019. "Model-based computed tomography image estimation: partitioning approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(14), pages 2627-2648, October.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2627-2648
    DOI: 10.1080/02664763.2019.1606169
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