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Title
PET image segmentation using a Gaussian mixture model and Markov random fields
AuthorLayer, Thomas ; Blaickner, Matthias ; Knäusl, Barbara ; Georg, Dietmar ; Neuwirth, Johannes ; Baum, Richard P. ; Schuchardt, Christiane ; Wiessalla, Stefan ; Matz, Gerald In der Gemeinsamen Normdatei der DNB nachschlagen
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EJNMMI Physics, Berlin, 2015, Vol. 2, Issue 9, page 1-15
PublishedSpringer Open, 2015
Edition
Published version
LanguageEnglish
Document typeJournal Article
Keywords (EN)Expectation maximization / Markov random field / Positron emission tomography / Radiotherapy / Tumor segmentation
ISSN2197-7364
URNurn:nbn:at:at-ubtuw:3-57 Persistent Identifier (URN)
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 The work is publicly available
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PET image segmentation using a Gaussian mixture model and Markov random fields [1.11 mb]
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Abstract (English)

Background: Classification algorithms for positron emission tomography (PET) images support computational treatment planning in radiotherapy. Common clinical practice is based on manual delineation and fixed or iterative threshold methods, the latter of which requires regression curves dependent on many parameters.

Methods: An improved statistical approach using a Gaussian mixture model (GMM) is proposed to obtain initial estimates of a target volume, followed by a correction step based on a Markov random field (MRF) and a Gibbs distribution to account for dependencies among neighboring voxels. In order to evaluate the proposed algorithm, phantom measurements of spherical and non-spherical objects with the smallest diameter being 8mm were performed at signal-to-background ratios (SBRs) between 2.06 and 9.39. Additionally 68Ga-PET data from patients with lesions in the liver and lymph nodes were evaluated.

Results: The proposed algorithm produces stable results for different reconstruction algorithms and different lesion shapes. Furthermore, it outperforms all threshold methods regarding detection rate, determines the spheres volumes more accurately than fixed threshold methods, and produces similar values as iterative thresholding. In a comparison with other statistical approaches, the algorithm performs equally well for larger volumes and even shows improvements for small volumes and SBRs. The comparison with experts manual delineations on the clinical data shows the same qualitative behavior as for the phantom measurements.

Conclusions: In conclusion, a generic probabilistic approach that does not require data measured beforehand is presented whose performance, robustness, and swiftness make it a feasible choice for PET segmentation.

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CC-BY-License (4.0)Creative Commons Attribution 4.0 International License