Detection of Pulmonary Nodules in CT Images Using Template Matching and Neural Classifier

Computer aided pulmonary nodule detection has been among major research topics lately to help the early treatment of lung cancer which is the most lethal kind of cancer worldwide. Some evidence suggests that periodic screening tests with the CT of patients will help in reducing the mortality rate caused by the lung cancer. A complete and accurate computer aided diagnosis (CAD) system for detection of nodules in lung CT images consists of three main steps: extraction of lung parenchyma, candidate nodule detection and false positive reduction. While precise segmentation of lung region speed upthe detection process of pulmonary nodules by limiting the search area, in candidate nodule detection step we attempt to include all nodule like structures. However, the main problem in the current CAD systems for nodule detection is the high false positive rate which is mostly associated to misrecognition of juxta-vascular nodules from blood vessels. In this paper we propose an automated method which has all of the three above mentioned steps. Our method attempts to find initial nodules by thresholding and template matching. To separate false positives from nodules, we use feature extraction and neural classifier. The proposed method has been evaluated against several images in LIDC database and the results demonstrate improvements in comparison with the previous methods.