Overview of Advanced Computerized Methods for Lung Cancer Detection

Computerized Detection of Lung cancer has been widely used for the last two decades. Image processing techniques provide a good tool for improving the manual screening of CT samples of lung. Processing of pulmonary X-ray computed tomography (CT) images is a predecessor to most of the pulmonary image analysis applications such as cancer and TB detection. The automated extraction of the lung cancer in CT images is the most crucial step in a computer-aided diagnosis (CAD) system. The CAD system is equipped with functions that automatically detect the suspicious regions from chest CT images from the Region of Interest. Significant advancements have been made in this area for the last few years. Automating the analysis of such CT data is a necessary task. This automation has created a rapidly developing research area in the field of medical imaging. This paper presents a state of art survey of various image processing methods and techniques for computerized detection of lung cancer in CT images. This paper focuses on various CAD methods for lung cancer detection such as the Lung Segmentation, Segmentation of Airways, vessels and the Segmentation of the Lobes and Fissures. In addition, research directions and future challenges focusing towards a better CAD scheme was also discussed.