Automated Detection of Pulmonary Nodules in CT Scans

In this paper, we describe a computer-aided diagnosis (CAD) system for automated detection of pulmonary nodules in computed-tomography (CT) examinations. After a segmentation phase based on the robust fuzzy c-means (RFCM) algorithm proposed by Pham, the regions of interest (ROIs) undergo both 2D and 3D morphological analysis in order to distinguish between nodule and blood vessel sections. The system has been applied to eight CT scans, including four at high-dose radiation and four at low-dose radiation, with a total of about 2400 digital images. Three of the eight scans are pertinent to as many patients suffering from lung cancer. A total of 10 malignant nodules are present in these scans, ranging from 4 mm to 10 mm in diameter. We achieved no false negatives (i.e., true nodules that are not found by the algorithm) and an average of approximately two false-positives (i.e., non-nodules recognized as nodules) per CT image