Robust quantification of pulmonary emphysema with a Hidden Markov Measure Field model

Determining the extent of pulmonary emphysema with quantitative computed tomography commonly relies on fixed intensity threshold values. However, the reliability of such measures is limited due to variability in parenchymal intensities and noise levels in CT images. In this work, we present a novel method for emphysema quantification, based on a lung tissue segmentation with a Hidden Markov Measure Field model. By adapting to the intensity distribution present in the input image, the method provides a more robust emphysema index than standard densitometric approaches. The focus of this study is to show robustness between imaging protocols, enabling the comparison of emphysema measures between studies. The method can have a significant impact in longitudinal analysis and prediction of emphysema. In addition, the method shows promise in delineating emphysematous regions, potentially facilitating subtyping of the disease.