Early and accurate diagnosis of lung cancer is one of the most investigated open challenges in the last decades. The diagnosis for this cancer type is usually lethal if not detected in early stages. For these reasons it is clear the need of creating an automated diagnostic tool that requires less time for the identification and does not require a cross-validation of the results by different radiologist, being in this way cheaper and less error prone. The aim of this work is to implement a completely automated pipeline that starting from the current imaging technologies, such as Computed Tomography (CT) and Positron Emission Tomography (PET), will identify lung cancer to be employed for the staging; moreover, it will be a suitable starting point for a machine learning based classification procedure. In particular, this project proposes both a methodology and the related software tool that taking as input Digital Imaging and COmmunications in Medicine (DICOM®) files of chest PET and CT and by exploiting the characteristics of both of them is capable of automatically identify the lungs and the eventually presence of tumor lesions. A validation of the image processing pipeline has been done by computing the execution time and the reached accuracy. The obtained accuracy varies between 89-97% on the analyzed dataset with a significant reduction of the analysis time.