Objective: To automatically segment cell nuclei in histology images of bladder and skin tissue for karyometric analysis. Materials/Methods: The four main steps in the program were as follows: 1) median filtering and thresholding, 2) segmentation, 3) categorizing, and 4) cusp correction. This robust segmentation technique used properties of the image histogram to optimally select a threshold and create closed four-way chain code nuclei segmentations. Each cell nucleus segmentation was treated as an individual object with properties of segmentation quality. A segmentation was placed in one of the following three categories based on its properties: throw away, salvageable, or good. Erosion/dilation and rethresholding were performed on salvageable nuclei to correct cusps. Results: Ten bladder histology images were segmented both by hand and using this automatic segmention algorithm. The automatic segmentation resulted in a sensitivity of 76.4%. The average difference between hand and automatic segmentations over 42 nuclei, calculated for each of the 95 features used in karyometric analysis, ranged between 0 and 48.3%, with an average of 2.8%. The same procedure was performed on 10 skin histology images with a sensitivity of 83.0%. Average differences over 44 nuclei ranged between 0 and 200%, with an average of 10.0%. Conclusion: The close agreement in karyometric features with hand segmentation shows that automated segmentation can be used for analysis of bladder and skin histology images. Average differences between hand and automatic segmentations were smaller in bladder histology images because these images contained less contrast, and therefore the range of the karyometric feature values was smaller.