Segmentation of Intestinal Gland Images with Iterative Region Growing

A region growing algorithm for segmentation of human intestinal gland images is presented. The initial seeding regions are identified based on the large vacant regions (lumen) inside the intestinal glands by fitting with a very large moving window. The seeding regions are then expanded by repetitive application of a morphological dilate operation with a much smaller round window structure set. False gland regions (nongland regions initially misclassified as gland regions) are removed based on either their excessive ages of active growth or inadequate thickness of dams formed by the strings of goblet cell nuclei sitting immediately outside the grown regions. The goblet cell nuclei are then identified and retained in the image. The gland contours are detected by applying a large moving round window fitting to the enormous empty exterior of the goblet cell nucleus chains in the image. The assumptions based on real intestinal gland images include the closed chain structured goblet cell nuclei that sit side-by-side with only small gaps between the neighbouring nuclei and that the lumens enclosed by the goblet cell nucleus chains are most vacant with only occasional run-away nuclei. The method performs well for most normal and abnormal intestinal gland images although it is less applicable to cancer cases. The experimental results show that the segmentations of the real microscopic intestinal gland images are satisfactorily accurate based on the visual evaluations.