Hybrid lung segmentation in chest CT images for computer-aided diagnosis

We propose an automatic segmentation method for accurately identifying lung surfaces in chest CT images. Our method consists of three steps. First, lungs and airways are extracted by an inverse seeded region growing and connected component labeling. Second, trachea and large airways are delineated from the lungs by three-dimensional region growing. Third, accurate lung region borders are obtained by subtracting the result of the second step from that of the first step. The proposed method has been applied to 10 patient datasets with lung cancer or pulmonary embolism. Experimental results show that our segmentation method extracts lung surfaces automatically and accurately. Averaged over all volumes, the root mean square difference between the computer and manual analysis is 1.2 pixels.