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Automated and accurate segmentation of the aorta in 4D (3D+time) cardiovascular magnetic resonance (MR) image data is important for early detection of congenital aortic disease leading to aortic aneurysms and dissections. A computer-aided diagnosis (CAD) method is reported that allows one to objectively identify subjects with connective tissue disorders(More)
Automated and accurate segmentation of the aorta in 4D (3D+time) car-diovascular magnetic resonance (MR) image data is important for early detection of congenital aortic disease leading to aortic aneurysms and dissec-tions. An automated 4D segmentation method is reported in this study. Our automated segmentation method combines level-set and optimal surface(More)
Extremely hot events (usually involving a few hours at extreme high temperatures in summer) are expected to increase in frequency in temperate regions under global warming. The impact of these events is generally overlooked in insect population prediction, since they are unlikely to cause widespread mortality, however reproduction may be affected by them.(More)
This paper describes a system for detecting pulmonary nodules in CT images. It aims to label individual image voxels in accordance to one of a number of anatomical (pulmonary vessels or junctions), pathological (nodules), or spurious (noise) events. The approach is orthodoxly Bayesian, with particular care taken in the objective establishment of prior(More)
Tree-like vessel structures are an information-rich source for many image analysis tasks. Hence tracking algorithms extracting such structures have wide applicability. However, due to image artifacts and the minute nature of vessels, these algorithms face several challenges; two of the most common ones are 1) early termination, where tracking stops before(More)