Automatically Detecting Lung Nodules Based on Shape Descriptor and SemiSupervised 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 ROI 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.
lung nodules detection computer aided diagnosis semi-supervised learning
Yang Liu Zhian Xing Chao Deng Ping Li Maozu Guo
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China China Mobile Research Institute, Beijing, China Department of Radiology, the Second Affiliated Hospital of Harbin Medical University,Harbin, China
国际会议
太原
英文
647-650
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）