Bayesian belief networks in quantitative histopathology

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....

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Bibliographic Details
Published inAnalytical and quantitative cytology and histology Vol. 14; no. 6; p. 459
Main Authors Bartels, P H, Thompson, D, Bibbo, M, Weber, J E
Format Journal Article
LanguageEnglish
Published United States 01.12.1992
Subjects
Bayes Theorem
Expert Systems
Histological Techniques
Humans
Image Processing, Computer-Assisted
Male
Prostate - pathology
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Summary: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.
ISSN:0884-6812