Automatically detecting lung nodules based on shape descriptor and semi-supervised learning

Computer-aided diagnosis (CAD) has become a major research topic in medical imaging, and one of the most important CAD applications is the detection of lung nodules. The paper is to develop a CAD system for automatically detecting lung nodules in computed tomography (CT) images. The system includes three parts: pulmonary parenchyma segmentation, ROI extraction, and nodule prediction of ROI based on ADE-Co-Forest. At the beginning, we proposed the new pulmonary parenchyma segmentation method; In the stage of RDI extraction, circle shape descriptor is exploited to reduce the false positives; Although the samples can be easily collected from routine medical examinations, it is usually impossible for medical experts to make a diagnosis for each of the collected samples. So we use the semi-supervised learning method ADE-Co-Forest to predict the nodules. Thus, in the predicting stage, we can use a few of labeled samples and a lot of unlabeled samples to learn a well-performed classifier. The experimental results demonstrate that the CAD system gets high sensitivity and low false-positive.