Segmentation of intestinal gland images with iterative region growing

Abstract

SummaryA 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.

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

“…When dealing with large-scale histopathological data, this unified framework can be quite efficient. With one forward propagation, it can generate the results of gland objects and contours simultaneously instead of resorting to additional post-separating steps by generating contours based on lowlevel cues [20,38].…”

“…Broadly speaking, previous studies in the literature can be categorized into two classes: (1) pixel based methods. For this kind of method, various hand-crafted features including texture, color, morphological cues and Haar-like features were utilized to detect the glandular structure from histology images [11,38,13,36,37,28,23,32]; (2) structure based methods. Most of approaches in this category take advantage of prior knowledge about the glandular structure, such as graph based methods [2,20], glandular boundary delineation with geodesic distance transform [16], polar space random field model [18], stochastic polygons model [35], etc.…”

Tissue object patterns for segmentation in histopathological images

“…To this end, we extract features from each gland candidate and learn elimination rules on these features with a decision tree classifier. The features include the area and the percentage of the pixels that were previously quantized as white, pink, and purple using the k-means al- [12] 83.4 ± 7.7 92.3 ± 5.8 88.0 ± 4.2 85.8 ± 6.7 89.1 ± 10.4 87.6 ± 5.0 Nuclei-identification [7] 55.9 ± 28.5 55.2 ± 32.5 56.3 ± 18.3 53.8 ± 25.7 51.7 ± 33.6 53.2 ± 13.6 Lumina-identification [8] 47.2 ± 29.8 92.7 ± 8.4…”

“…The second group is cell/gland segmentation, in which cells or glands are located on a given tissue image. For gland segmentation, it has been proposed to classify the image pixels using their intensity and/or textural features and apply simple techniques such as thresholding and morphological operators to these classified pixels for constructing glandular regions [7,8,9]. All these studies use features extracted at the pixel level and do not incorporate medical background knowledge into their segmentation.…”

Segmentation of Brain Immunohistochemistry Images Using Clustering of Linear Centroids and Regional Shapes

“…[1][2][3][4][5] Nucleus segmentation that separates the nucleus regions from other part of the images can provide diagnostically important information such as the nucleus sizes and shapes. 6,7 Nucleus segmentation also enables the subsequent image analysis to be performed solely in the nucleus regions without the interference of the insignificant image background.…”