title: A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images.
abstract: 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.
authors.affiliation: Department of Radiology, University of California Davis Medical Center, Sacramento 95817, USA.
authors.forename: J
authors.initials: J
authors.lastname: Duryea
authors.forename: J M
authors.initials: JM
authors.lastname: Boone
mesh_terms: D000465:Algorithms; D001331:Automation; D003964:Diaphragm / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008433:Mathematics; D018482:Muscle, Skeletal / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity