title: Knowledge-guided segmentation of colorectal histopathologic imagery.
abstract: 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.
authors.affiliation: Optical Sciences Center, University of Arizona, Tucson 85721.
authors.forename: D
authors.initials: D
authors.lastname: Thompson
authors.forename: P H
authors.initials: PH
authors.lastname: Bartels
authors.forename: H G
authors.initials: HG
authors.lastname: Bartels
authors.forename: P W
authors.initials: PW
authors.lastname: Hamilton
authors.forename: J M
authors.initials: JM
authors.lastname: Sloan
mesh_terms: 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