PET image segmentation using a Gaussian mixture model and Markov random fields

摘要

BackgroundClassification 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.