Objective: To automatically segment cell nuclei in histology images of bladder and skin tissue for karyometric analysis.
Study design: The 4 main steps in the program were as follows: median filtering and thresholding, segmentation, categorizing and cusp correction. This robust segmentation technique used properties of the image histogram to optimally select a threshold and create closed 4-way chain code nuclear segmentations. Each cell nucleus segmentation was treated as an individual object of which the properties of segmentation quality were used for criteria to classify each nucleus as: throw away, salvageable or good. An erosion/dilation procedure and rethresholding were performed on salvageable nuclei to correct cusps.
Results: Ten bladder histology images were segmented both by hand and using this automatic segmentation algorithm. The automatic segmentation resulted in a sensitivity of 76.4%, defined as the percentage of hand-segmented nuclei that were automatically segmented with good quality. The median proportional difference between hand and automatic segmentations over 42 nuclei each with 95 features used in karyometric analysis was 1.6%. The same procedure was performed on 10 skin histology images with a sensitivity of 83.0% and median proportional difference of 2.6%.
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.