Quantitative computed tomography applied to interstitial lung diseases

Highlights: • CT density curves contain more disease knowledge that was not extracted so far. • Mathematical tools can help to decode invisible disease information from histograms. • Combinations of different image markers outperform individual methods. • On its own, the HFS concept achieves the highest correct disease classification. - Abstract: ObjectivesTo evaluate a new image marker that retrieves information from computed tomography (CT) density histograms, with respect to classification properties between different lung parenchyma groups. Furthermore, to conduct a comparison of the new image marker with conventional markers.