Segmentation of dynamic emission tomography data in projection space
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Lung tumor segmentation in PET images using graph cuts
Computer Methods and Programs in Biomedicine
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This paper investigates the segmentation of different regions in PET images based on the feature vector extracted from the time-activity 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.