We have applied automated image analysis methods in the assessment of human kidney perfusion based on 3D dynamic contrast-enhanced MRI data. This approach consists of non-rigid 3D image registration of the moving kidney followed by k-means clustering of the voxel time courses with split between left and right kidney. This method was applied to four data sets acquired from healthy volunteers, using 1.5 T (2 exams) and 3 T scanners (2 exams). The proposed registration method reduced motion artifacts in the image time series and improved further analysis of the DCE-MRI data. The subsequent clustering to segment the kidney compartments was in agreement with manually delineations (similarity score of 0.96) in the same motion corrected images. The resulting mean intensity time curves clearly show the successive transition of contrast agent through kidney compartments (cortex, medulla, and pelvis). The proposed method for motion correction and kidney compartment segmentation might improve the validity and usefulness of further model-based pharmacokinetic analysis of kidney function in patients.