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A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images
Author(s) -
Duryea Jeff,
Boone John M.
Publication year - 1995
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.597539
Subject(s) - digital radiography , segmentation , radiography , lung , image segmentation , artificial intelligence , human lung , computed radiography , observer (physics) , perspective (graphical) , algorithm , computer science , medicine , computer vision , radiology , mathematics , pattern recognition (psychology) , image (mathematics) , physics , image quality , quantum mechanics
A completely automated algorithm is presented which is capable of identifying both the right‐ and left‐lung fields on digitized chest radiographic images. The algorithm is tested on a sample of 802 chest images against lung fields drawn by a human observer. The average accuracies are found to be 0.957±0.003 and 0.960±0.003 for right‐ and left‐lung regions, respectively. To put them into perspective, the results are compared to several other simple segmentation techniques. These include a comparison of two sets of lung fields drawn by the human observer at different times which yielded accuracies of 0.967±0.005 and 0.967±0.004 for right‐ and left‐lung regions, respectively.