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Many of the diseases affecting the cardiovascular system include a variety of disorders and conditions that are related in part to the haemodynamics, as well as genetic predisposition and biochemistry amongst others. With respect to the haemodynamics, the commonly sought factors are near-wall mechanical properties including wall shear stress (and derived parameters) and transport phenomena, such as mixing and mass transport. These factors are susceptible to large variations amongst individuals, and in order to perform accurate clinical evaluation careful interpretation of patient specific information is required. Taking an example of a configuration of the aorto-illiac bifurcation, we examine the effects of image filtering and contrast enhancement on the reconstructed geometry and the resulting computed haemodynamics. The algorithms used to quantify the processed images are based on pixel intensity variance, peak signal-to-noise ratio and segmentation. In this study we focus on the effects of uncertainty in clinically acquired medical images to the variability in the reconstructed vessel geometry, and the subsequent error propagation to the computed haemodynamics with emphasis on factors related to diseased states.