Classification of margin characteristics from 3D pulmonary nodules

Detection of pulmonary nodules has played a significant role in lung cancer diagnosis because nodules are the first suspicious symptoms for the likelihood of cancer. Margin characteristics of the pulmonary nodules provide essential radiological features to determine the possibility of malignancy. In general, benign nodules hold quite smooth margins whilst malignant ones hold irregular margins. The main objective of this research is to classify different margin types of pulmonary nodules by observing the 3D structure. Nodule candidates from 2D lung CT slices are segmented firstly and then stacked to form a 3D image. Geometric features of the 3D nodule are extracted and fed into the support vector machine (SVM) classifier to classify the margin types. The proposed method can provide the classification accuracy of 90.9%.