Segmentation of lung tissue in CT images with disease and pathology

Lung segmentation is an important first step for quantitative lung CT image analysis and computer aided diagnosis. However, accurate and automated lung CT image segmentation may be made difficult by the presence of the abnormalities. Since many lung diseases change tissue density, resulting in intensity changes in CT image data, intensity-only segmentation algorithms will not work for most pathological lung cases. This thesis presents two automatic algorithms for pathological lung segmentation. One is based on the geodesic active contour, another method uses graph search driven by a cost function combining the intensity, gradient, boundary smoothness, and the rib information. The methods were tested on several 3D thorax CT data sets with lung disease. Given the manual segmentation result as gold standard, we validate our methods by comparing our automatic segmentation results with Hu's method. Sensitivity, specificity, and Hausdorff distance were calculated to evaluate the methods.