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Digital Knowledge and Diagnostic Information

Published online by Cambridge University Press:  02 July 2020

Peter H. Bartels
Affiliation:
Optical Sciences Center, University of Arizona, Tucson, AZ.
Deborah Thompson
Affiliation:
Optical Sciences Center, University of Arizona, Tucson, AZ.
Rodolfo Montironi
Affiliation:
Institute of Pathological Anatomy and Histopathology, University of Ancona, Ancona, Italy
Peter W. Hamilton
Affiliation:
The Queen's University, Belfast, Northern Ireland U.K.
Gian M. Mariuzzi
Affiliation:
Department of Pathology, University of Verona, Verona, Italy
Vinicius Duval da Silva
Affiliation:
Federal University of Rio Grande do Sul, Porto Alegre, Brazil
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Extract

Digital representation of diagnostic imagery offers mensuration, numeric data for diagnostic clue expression, objective assessment, and the option to define standards. Quantitative measurement allows the detection and documentation of very small differences and of diagnostic information that is visually not perceived. The automated extraction of such information from microscopic imagery is beginning to yield to knowledge guided image processing and the development of image understanding systems for machine vision. In a machine vision system, knowledge guidance may be provided by an expert system that controls a top to bottom scene segmentation with constant checks on local bottom-up derived segmentation results for compliance with model specifications and final scene reconstruction. Knowledge guidance is based on a knowledge file for the fully autonomous processing of scenes from a given domain. The knowledge file includes all entities representing traditional diagnostic and histologic terms and concepts: epithelium, stroma, lumen, nucleus, secretory cell, basal cell, stroma cell, chromatin, to name a few.

Type
Advanced Microscopy and Image Analysis of Cells and Tissue
Copyright
Copyright © Microscopy Society of America

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