Early detection of lung cancer

The clinical diagnostics for lung cancer are mostly depended on physical and biochemical techniques. Imaging processes in computed tomography (CT) screening (low-dose computed tomography - LDCT) is convenient for discovering lung cancer in the early stages. CT scan images for this study were obtained from Ankara Atatürk Training and Research Hospital and also from the online international database of "The Lung Image Database Consortium (LIDC)". Correct decision for diagnosis of lung cancer using CT scanning requires some processes to remove noise from image in enhancement stage. The noise removing process in this thesis have been proposed using a gradient magnitude in sobel filter. Finding edges on the images is a first step to detect nodule in the tissues. As well as following morphology operations to isolate background from the foreground is very important because background image represents the tissue of lung to locate a tumor on it, therefore foreground is unnecessary. Second part in the thesis includes usage of watershed algorithm for segmentation of tumor from the tissue. Labeling nodular irregular area inside the tissue leads to over-segmentation on image by connected components and markers which have different values as intensity values and regional minimum represented in the foreground. Markers classify tumor area by labeling high intensity values, locating the region of interest in normal image for cutting random area from the tissue. Distinguishing the normal and abnormal images that needs to use statistical methods is depended on the cancer type. To get feature extraction of nodule shape provides certain parameters which is an essential step for classification processes. Last part in this thesis focuses on classification processes on a dataset which includes values belong to five parameters that were taken from statistical method results. This dataset involves 306 images consisting of 153 normal images and 153 abnormal images. This database is implemented in nine classification algorithms and different results of accuracy performance were taken according to the theory of these algorithms. This study aims detecting tumor area and getting high performance accuracy after classification were done to find out the best algorithm to help the doctor for the diagnosis. Keywords: Lung cancer, early detection, sobel filter, morphology operations, watershed algorithm