Diagnosis and monitoring of kidney transplant allografts is supported by microscopic analysis of renal biopsy samples. Visual analysis by pathologists allows for incon­sistencies, bias, and inaccuracies; image analysis via digital processing can address these concerns, reduce effort, and potentially provide a second opinion. In this thesis, digital image analysis methods for automatic segmentation of structures in images of renal biopsy samples are presented. 1/Iethods for accurate segmentation of the effective biopsy area include opening by reconstruction, morphological closing, and erosion. The re ults were compared to contours drawn by an experienced pathologist; the mean distance to the closest point was 5.46 ± 3.92 µm (6 ± 4.31 pixels) and the true-positive fraction was 98.25 ± 1.77%. Methods for automatic segmentation of cell nuclei are also presented, including, automatic thresholding, adaptive threshold­ing, and morphological granulometry. The results were verified against pathologist annotations; true-positive ratios were in the range of 0.80 to 0.93.