Emission tomography such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) can provide in vivo measurements of dynamic physiological and biochemical processes in humans. A limitation of both PET and SPECT is their inability to provide precise anatomic localisation due to relatively poor spatial resolution and high noise levels when compared to magnetic resonance (MR) imaging. Manual placement of regions of interest (ROIs) is commonly used in clinical and research settings in analysis of PET and SPECT data. However, this approach is operator dependent and time-consuming. Semi- or fully-automated ROI delineation (or segmentation) methods offer advantages by reducing operator error and subjectivity and thereby improving reproducibility. In this paper, we describe an approach to automatically segment dynamic PET images based on functional difference using cluster analysis, and we validate our approach with a simulated phantom study and assess its performance in segmentation of dynamic lung data. Our results suggest that cluster analysis can be used to automatically segment tissues in dynamic PET and SPECT studies
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