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Renal diseases, caused by e.g. diabetes mellitus, hypertension or multiple cyst formations, can lead to kidney failure that requires renal replacement therapy (RRT). Early detection and treatment can delay or prevent this progression towards end-stage renal disease (ESRD). Worldwide an increasing number of people will in the near future suffer from ESRD, with dialysis or kidney transplantation as the costly therapeutic alternatives. In a clinical setting, the detection of renal failure (i.e. reduction in glomerular filtration rate, GFR) is a challenge, and today's methods (e.g. elevated serum creatinine and urine analysis) are very crude and cannot even differentiate between left and right kidney function. Magnetic resonance imaging methods such as DCE-MRI have proven to be a very promising tool for (semi)quantitative and localized assessment of renal function, representing a non-invasive procedure that most patients can tolerate. In order to fully realize the potential of data from such methods, a series of image processing and analysis steps are required, including image registration, segmentation, kidney compartment modeling and visualization. We present here methods and results from these steps, and describes how the processing pipeline has been integrated as a software prototype.