Automatic detection of lung nodules from multi-slice low-dose CT images

We describe in this paper a novel, efficient method to automatically detect lung nodules from low-dose, high-resolution CT (HRCT) images taken with a multi-slice scanner. First, the program identifies initial anatomical seeds, including lung nodule candidates, airways, vessels, and other features that appear as bright opacities in CT images. Next, a 3D region growing method is applied to each seed. The thresholds for segmentation are adaptively adjusted based upon automatic analysis of the local histogram. Once an object has been examined vessels and other non-nodule objects are quickly excluded from future study, thus saving computation time. Finally, extracted 3D objects are classified as nodule candidates or non-nodule structures. Anatomical knowledge and multiple measurements, such as volume and sphericity, are used to categorize each object. The detected nodules are presented to the user for examination and verification. The proposed method, was applied to 14 low dose HRCT patient studies. Since the CT images were taken with a multi-slice scanner, the average number of slices per study was 292. In every case the x-ray exposure was about 20 mAs, a suitable dosage for screening. In our preliminary results, the, method detected an average of 8 nodules per study, with an average size of 3.3 mm in diameter.