Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images.
We present a knowledge-based approach to segmentation and analysis of the lung boundaries in chest X-rays. Image edges are matched to an anatomical model of the lung boundary using parametric features. A modular system architecture was developed which incorporates the model, image processing routines, an inference engine and a blackboard. Edges associated with the lung boundary are automatically identified and abnormal features are reported. In preliminary testing on 14 images for a set of 18 detectable abnormalities, the system showed a sensitivity of 88% and a specificity of 95% when compared with assessment by an experienced radiologist.
Brown
M S
MS
Department of Radiological Sciences, School of Medicine, University of California, Los Angeles, USA. mbrown@endeavour.radsci.ucla.edu
Wilson
L S
LS
Doust
B D
BD
Gill
R W
RW
Sun
C
C
D000465:Algorithms; D001185:Artificial Intelligence; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000033:anatomy & histology* / Q000000981:diagnostic imaging* / Q000473:pathology; D008953:Models, Anatomic; D013902:Radiography, Thoracic; D012680:Sensitivity and Specificity