A new efficient 2D combined with 3D CAD system for solitary pulmonary nodule detection in CT images

Lung cancer has become one of the leading causes of death in the world. Clear evidence shows that early discovery, early diagnosis and early treatment of lung cancer can significantly increase the chance of survival for patients. Lung Computer-Aided Diagnosis (CAD) is a potential method to accomplish a range of quantitative tasks such as early cancer and disease detection. Many computer- aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3Dmethods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand. Finally, all malignant nodules were detected and a very low false-positive detection rate was achieved. The automated extraction of the lung in CT images is the most crucial step in a computer-aided diagnosis (CAD) system. In this paper we describe a method, consisting of appropriate techniques, for the automated identification of the pulmonary volume. The performance is evaluated as a fully automated computerized method for the detection of lung nodules in computed tomography (CT) scans in the identification of lung cancers that may be missed during visual interpretation. Index Terms—Lung Cancer, Computer-aided Diagnosis, Solitary pulmonary nodules, 3D, Automated