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Automated microscopy in diagnostic histopathology: From image processing to automated reasoning
Author(s) -
Bartels Peter H.,
Gahm Thomas,
Thompson D.
Publication year - 1997
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/(sici)1098-1098(1997)8:2<214::aid-ima8>3.0.co;2-d
Subject(s) - histopathology , computer science , artificial intelligence , segmentation , inference , image processing , pattern recognition (psychology) , radiology , pathology , computer vision , medicine , image (mathematics)
A machine vision system for diagnostic histopathology offers five modules: 1) for the automated detection of regions of abnormality in histopathologic sections; 2) for fully automated image segmentation and diagnostic information extraction by a knowledge‐guided procedure; 3) for the derivation of histometric indices, such as a progression index or grade for a lesion; 4) for diagnostic evidence evaluation by Bayesian inference networks; and 5) for individual patient targeted prognosis based on a case‐based reasoning process. The system has been in operation for several years. Correct segmentation for even complex scenes such as cribriform glands has been achieved with a high success rate for histopathologic sections from prostate, colon, and breast. The lesion search module and prognostic module have passed feasibility testing and are still undergoing development. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 214–223, 1997