Early lung cancer detection using nucleus segementation based features

In this study we propose an early lung cancer detection methodology using nucleus based features. First the sputum samples from patients are labeled with Tetrakis Carboxy Phenyl Porphine (TCPP) and fluorescent images of these samples are taken. TCPP is a porphyrin that is able to assist in labeling lung cancer cells by increasing numbers of low density lipoproteins coating on the surface of cancer. We study the performance of well know machine learning techniques in the context of lung cancer detection on Biomoda dataset. We obtained an accuracy of 81% using 71 features related to shape, intensity and color in our previous work. By adding the nucleus segmented features we improved the accuracy to 87%. Nucleus segmentation is performed by using Seeded region growing segmentation method. Our results demonstrate the potential of nucleus segmented features for detecting lung cancer.