Towards generalized nuclear segmentation in histological images

Abstract

Computer aided diagnosis in cancer pathology (computational pathology) using histological images of biopsies is an emerging field. Segmentation of cell nuclei can be an important step in such image processing pipelines. Although seeded watershed segmentation is a simple and computationally efficient segmentation technique, it is prone to errors like over-segmentation when applied to histological images. We report specific enhancements to this technique to improve segmentation of cell nuclei in histological images. Foreground seeds were generated by fast radial symmetry transform (FRST). Otsu thresholding was used on enhanced image to estimate tentative foreground map. Background markers were computed from the tentative foreground map. False detections in the segmented output were removed by logical AND with the tentative foreground map. Using these enhancements nuclear segmentation was significantly improved on histological images (H&E stained breast and intestinal tissue images, Feulgen stained images of prostate tissues).