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Updated: Jul 13, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images
M S Brown1, L S Wilson, B D Doust
1Department of Radiological Sciences, School of Medicine, University of California, Los Angeles, USA. mbrown@endeavour.radsci.ucla.edu
Abstract:
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.
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