Lung air estimation using non-invasive techniques can be used for assessment of the lung function and effective diagnosis of lung disease (e.g. chronic obstructive pulmonary disease). A novel technique is proposed to automatically estimate the lung air volume and its variations throughout respiration cycle using 4D thoracic CT images. The technique is based on an advanced thresholding method which benefits from parenchyma tissue mechanical properties in addition to the lung image data over the respiration cycle to determine the segmentation threshold values. The technique was applied to CT scans of ex vivo porcine lung and to lung images of COPD patients. Results indicate that the air volume estimated using the proposed method is significantly more accurate when AVPCs are used in the air volume estimation as mean errors of only 1% and less than 6.86% were obtained in the ex vivo lung and patient studies, respectively. Furthermore, a novel expert system technique is proposed to accurately assess COPD severity characterized by its stage through processing the patient’s thoracic CT images. The technique inputs thoracic CT images to automatically extract 23 features of air volume variation and distribution within the lung over respiration cycle. Relationships between features and pulmonary function test (PFT) measurements were developed which indicated strong correlation. Results obtained in this investigation showed an overall accuracy of over 84% which demonstrates its effectiveness in determining COPD stage merely based on CT images and without using PFT measurements. The performance characteristics of the proposed method, including being completely images based, fully automatic combined with its demonstrated high accuracy makes it a strong candidate to complement/replace pulmonary function diagnostic tests that involve experimental measurement of function parameters. Key Words Air Volume Estimation, 3D CT Images, Segmentation, Lung, Partial Volume Effect, COPD
