Automated lung segmentation in digitized posteroanterior chest radiographs.
OBJECTIVE
The authors developed and tested a gray-level thresholding-based approach to automated lung segmentation in digitized posteroanterior chest radiographs.


METHODS
Gray-level histogram analysis was initially performed to establish a range of thresholds for use during an iterative global gray-level thresholding technique. Local gray-level threshold analysis was then performed on the output of global thresholding. The resulting contours were subjected to several smoothing processes, including a rolling-ball technique. The final contours closely approximated the boundaries of the aerated lung regions. The method was applied to a database of 600 posteroanterior chest images. Radiologists rated the accuracy and completeness of the contours with a five-point scale.


RESULTS
Results of the subjective rating evaluation indicated that this method was accurate, with 79% of the assigned ratings reflecting moderately or highly accurate segmentation and only 8% of the ratings indicating moderately or highly inaccurate segmentation.


CONCLUSIONS
This gray-level thresholding-based approach provides accurate automated lung segmentation in digital posteroanterior chest radiographs.
Armato
S G
SG
Department of Radiology, Kurt Rossmann Laboratories for Radiologic Image Research, University of Chicago, IL 60637, USA.
Giger
M L
ML
MacMahon
H
H
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement; D011873:Radiology Information Systems