title: Bayesian belief networks in quantitative histopathology.
abstract: Bayesian belief networks have a dynamic range and numeric response characteristics that make them uniquely suitable for descriptive classification schemes. Features showing considerable overlap of tolerance regions may be used, in a cumulative manner, to derive unequivocal classification decisions. The numeric response characteristics of Bayesian belief networks are analyzed, and their application as control modules in automated scene segmentation in histopathology is demonstrated.
authors.affiliation: Optical Sciences Center, University of Arizona, Tucson 85721.
authors.forename: P H
authors.initials: PH
authors.lastname: Bartels
authors.forename: D
authors.initials: D
authors.lastname: Thompson
authors.forename: M
authors.initials: M
authors.lastname: Bibbo
authors.forename: J E
authors.initials: JE
authors.lastname: Weber
mesh_terms: D001499:Bayes Theorem; D005103:Expert Systems; D006652:Histological Techniques; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D011467:Prostate / Q000473:pathology