Abstract
This paper investigates the segmentation of different regions in PET images based on the feature vector extracted from the timeactivity curve for each voxel. PET image segmentation has applications in PET reference region analysis and activation studies. The segmentation algorithm presented uses a Markov random field model for the voxel class labels. By including the Markov random field model in the expectation-maximisation iteration, the algorithm can be used to simultaneously estimate parameters and segment the image. Hence, the algorithm is able to combine both feature and spatial information for the purpose of segmentation. Experimental results on synthetic and real PET data are presented to demonstrate the performance of the algorithm. The algorithms used in this paper can be used to segment other functional images.
Acknowledgements
The authors wish to thank the Richard Banati and Ralph Myers at the Medical Research CouncilC yclotron Unit for discussions and the provision of data.
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Chen, J.L., Gunn, S.R., Nixon, M.S., Gunn, R.N. (2001). Markov Random Field Models for Segmentation of PET Images. In: Insana, M.F., Leahy, R.M. (eds) Information Processing in Medical Imaging. IPMI 2001. Lecture Notes in Computer Science, vol 2082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45729-1_50
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DOI: https://doi.org/10.1007/3-540-45729-1_50
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