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.

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, In computer-aided diagnosis of lung disease, accurate and fast pulmonary parenchyma segmentation is the core step. Watershed algorithm is used in this paper to segment and extract lung parenchyma. To reduce over-segmentation, an improved watershed segmentation method which marks foreground and background is proposed. This method is based on watershed algorithm using “Sobel” operator on edge detection and then using mathematical morphology opening and closing operations and morphological reconstruction to mark the foreground. Extracting the local maximum associated with object will constitute the binary marker image. By testing different types of images, it proposes that the algorithm in this paper can be consistent with human visual characteristics and get more accurate, continuous object boundary. Compared with other watershed improvement methods, this proposed one requires less computational complexity, more simple parameters, and can effectively reduce over-segmentation.