Knowledge-guided segmentation of colorectal histopathologic imagery.
Scene segmentation in images of sections of colorectal glands is guided by a knowledge-based system. The construction of the knowledge file, processing sequences and computation of a crowding index for adenomatous glands are described. The histologic sections were stained by hematoxylin and eosin, and the image segmentation was difficult. The knowledge-based system has to determine, on the basis of context and logic, which detected object groups belong to which histologic components. Image segmentation was followed by a reconstruction process. Acceptable segmentation was attained in approximately 85% of the glands processed.
Thompson
D
D
Optical Sciences Center, University of Arizona, Tucson 85721.
Bartels
P H
PH
Bartels
H G
HG
Hamilton
P W
PW
Sloan
J M
JM
D000236:Adenoma / Q000145:classification / Q000473:pathology*; D001185:Artificial Intelligence; D015179:Colorectal Neoplasms / Q000145:classification / Q000473:pathology*; D004848:Epithelium / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007413:Intestinal Mucosa / Q000473:pathology; D008853:Microscopy; D013194:Staining and Labeling