Nodule Detection on Chest Helical CT Scans by Using a Genetic Algorithm
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
WSEAS Transactions on Information Science and Applications
WSEAS Transactions on Information Science and Applications
WSEAS Transactions on Information Science and Applications
Decision fusion for improved automatic license plate recognition
WSEAS Transactions on Information Science and Applications
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In this paper, we develop a lung area extraction technique from X-ray computed tomography (CT) images for computer-aided diagnosis (CAD) systems. In lung cancer cases, pulmonary nodules are typical pathological changes and thus they are the target to be detected by CAD systems. The isolated nodules can be detected more easily by CAD systems developed previously, while previous CAD systems are often hard to detect nonisolated nodules. The extraction technique can then be used for transforming the non-isolated pulmonary nodules connected to the walls of the chest into isolated ones. The technique proposed here is based on an active contour model, but such model is often trapped into a local optimum solution. To avoid the local optimum solutions, an essential core of the proposed technique is to select an appropriate initial contour by using an anatomical feature of the lung shape in X-ray CT slices. Some experimental results demonstrate the usefulness of the proposed technique for assisting the CAD systems to detect non-isolated nodules more accurately.