Quantification of epithelial area by image processing applied to endometrial carcinomas: a comparison with ovarian tumors.
In endometrial carcinomas, the epithelial area measured by interactive morphometry is an important feature in the classification of tumors of varying histologic grades. This report describes an image analysis technique for the fully automated estimation of the area percentages of epithelium and stroma in tissue sections of the endometrium obtained from hysterectomy specimens. The method is evaluated using endometrial carcinomas with varying degrees of malignancy. From standard paraffin sections stained with pararosanilin Feulgen and naphthol yellow, a blue-yellow image pair was recorded. The blue image was used to determine the total tissue area and the yellow image was used to determine the epithelial area. Image processing of the blue image was comprised of correction for shading, segmentation of the tissue area, and restoration of the segmented image by removing small artefacts and closing small tears in the tissue. Image processing of the yellow image was based on the fact that epithelial nuclei are generally more tightly packed than stromal nuclei and consists of the following steps: correction for shading, gaussian blurring, segmentation of nuclei, and editing the segmented image by removing small objects and closing small spaces between the epithelial nuclei. These image processing steps are compared with those used for quantification of the epithelial area in ovarian tumors. The performance of the method was evaluated using 120 image pairs from 30 endometrial carcinomas of varying histologic grades. The epithelial area percentages, as assessed by digital image processing, strongly correlate to control percentages that were established by interactive morphometry (r = .987).
Schipper
N W
NW
Department of Pathology, Free University, Amsterdam, Netherlands.
Smeulders
A W
AW
de Lange
J H
JH
Baak
J P
JP
D002467:Cell Nucleus / Q000473:pathology; D004848:Epithelium / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D010051:Ovarian Neoplasms / Q000473:pathology*; D014594:Uterine Neoplasms / Q000473:pathology*