In this thesis, individual steps of a pipeline for processing of the peripheral Computed Tomography Angiography (CTA) datasets are addressed. The peripheral CTA datasets are volumetric datasets representing pathologies in vascularity of the lower extremities in the human body. These pathologies result from various atherosclerotic diseases as e.g. the Peripheral Arterial Occlusive Disease (PAOD) and their early and precise diagnostics significantly contributes to planning of a later interventional radiology treatment. The diagnostics is based on visualization of the imaged vascular tree, where individual pathologic changes, as plaque, calcifications, stenoses of the vessel lumen and occluded parts of the vessels are visible. CTA has evolved within the recent years into a robust, accurate and cost effective imaging technique for patients with both coronary and arterial diseases. As a result of the CTA scanning, a set of 1200–2000 transverse slices is acquired, depicting vessels enhanced by means of an intravenously injected contrast medium. The number of slices is high and therefore their manual examination is laborious and lengthy. As a remedy, post-processing methods were developed to allow faster and more intuitive visualization of the imaged vascularity. However, simple visualization by means of the traditional techniques as maximum-intensity projection (MIP) or direct volume rendering (DVR) is hampered due to the presence of bones in the dataset, which occlude the vessels
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