Background: One of the primary causes of tumor death in the world is lung cancer. Lung cancer is caused by abnormalities in the lung called nodules. Different medical imaging techniques are used to detect these nodules, like Chest X-ray, Computed Tomography (CT), etc.
Methods: Computer Aided Detection (CAD) in radiology provides a second opinion to radiologists in determining medical abnormalities by providing automated analysis of medical images. A standard lung cancer CAD system consists of five main processing steps: acquisition, pre-processing, lung segmentation, nodule detection and false positive reduction.
Results: This paper overviews some of the current state-of-the-art CAD systems and presents the algorithm used for each processing step.
Conclusion: It also provides a comparison of the performance of the existing approaches.