Efficient edge detection methods for diagnosis of lung cancer based on two- dimensional cellular automata

Lung cancer is one of the most serious health problems in the world. Lung Computer-Aided Diagnosis (CAD) is a potential method to accomplish a range of quantitative tasks such as early cancer and disease detection, analysis of disease progression. The basic goal of CAD is to provide a computer output as a second opinion to assist medical image interpretation by improving accuracy, consistency of diagnosis, and image interpretation time. Since a CAD system is only interested in analyzing a specific organ, edge detection of Computed Tomography (CT) images is a precursor to most image analysis applications. A fully automated method is presented to edge detection of lung CT scan images for diagnosis of lung cancer based on cellular automata. The proposed method is not only computational inexpensive, but also is robust and accurate in detecting lung borders.