Automatic Segmentation of Pulmonary Structures in Chest CT Images
We propose an automatic segmentation method for accurately identifying lung surfaces, airways, and pulmonary vessels in chest CT images. Our method consists of four steps. First, lungs and airways are extracted by inverse seeded region growing and connected component labeling. Second, pulmonary vessels are extracted from the result of first step by gray-level thresholding. Third, trachea and large airways are delineated from the lungs by three-dimensional region growing based on partitioning. Finally, accurate lung regions are obtained by subtracting the result of third step from the result of 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, airways, and pulmonary vessels automatically and accurately.
KeywordsAutomatic Segmentation Pulmonary Vessel Large Airway Connected Component Label Chest Compute Tomography Scan
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