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The primary goal of medical image segmentation is to partition the raw image into region of interests (ROIs) matching the anatomical localization of objects of interest in 2D or 3D space. The traditional method of ROI delineation (or segmentation) for the analysis of dynamic emission tomography is the manual placement of ROIs by the operator. However this approach is operator dependent, time-consuming and may lack good reproducibility. Quantitative positron emission tomography (PET) studies can provide measurements of dynamic physiological and biochemical processes in humans through the use of temporal kinetics available. However, due to the relatively poor spatial resolution and high noise levels, partitioning of ROIs is limited. In this paper, the use of a novel knowledge-based approach to segmentation of clinical PET studies using automatic seed selection for adaptive region growing based on Euclidean distance between the local tissue time-activity curves (TTAC) of the voxels is proposed.