TEXTURE FEATURE EXTRACTION TO IMPROVE ACCURACY OF MALIGNANT AND BENIGN CANCER DETECTION ON CT-SCAN IMAGES

Lung cancer is a type of lung disease characterized by uncontrolled cell growth in lung tissue, whereas nodules (benign cancer) are small, round or egg-shaped lesions in the lungs. The current method used to diagnose lung cancer from CT scan images is by observing a data set of 2-D CT Scan images using the naked eye, then interpreting data one by one. This procedure is certainly not sufficient. Research conducted aims to extract texture features to improve the accuracy of malignant and benign cancers detection in CT scans. This research covers 5 (five) main points. The first is pre-processing CT-Scan images. The second is the automatic segmentation of lung area using the Active Appearance Model (AAM) method. The third is the segmentation of candidates who are considered cancer using morphological mathematics. Fourth, the process of detecting benign and malignant lung cancer is using Support Vector Machine (SVM). The fifth is the visualization of malignant and benign lung cancer using Volume Rendering. Accuracy of malignant and benign cancers detection is 79.7%.