Lungs Three-dimensional Model Reconstruction And Recognition System

Lung cancer is the number one killer and threats to human health. At presnet, thedeath rate of lung cancer at home and abroad are soaring. Especially in the past halfcentury, the number of lung cancer patients is rank first. The study shows that we canfind the90%lung cancer lesions in the early days. But, by doctors alone to diagnosischest X-ray, it might have missed the diagnostic significance of solitary pulmonarynodules that up to a30%chance. With the rapid development of computer softwaretechnology and hardware technology, computer-aided design system are appear. It canprovide strong support for the detection and diagnosis of lung cancer. It can not onlyreconstruct and visual lung organ, and give the size of lung nodules, the position oflikely exist, but also help doctors to accurately judge and analysis the medical images.That is to say, it can provide a better repeatability and consistency "look and feel" to thedoctors, prevent misdiagnosis and missed diagnosis. Thereby, it can enhance the truepositive rate of lung cancer diagnosis and reduce the false positive rate of lung cancerdiagnosis. Making the results of doctors diagnosis are more accurate and reliable.The paper develops a set of lung three-dimensional model reconstruction andrecognition system by making use of sophisticated computer visualization technologyand statistical modeling technique. The system mainly includes four function modules:image preprocessing, organ segmentation and contour extraction, three-dimensionalreconstruction of organ and lung three-dimensional model identification. The system notonly can segment and extract the contour about any organ of the lung, but also canaccurately reconstruct it. It allows doctors to visualize observe lung lesions in order toeffectively improve the success rate of treatment. It has a very significant meaning forhuman life and health. The system can accurately mark the parts of the lung nodules atthe CT image and provide these suspected lesion to doctors to carefully diagnosis. Itgreatly reduces the number of doctors to read the CT image and improve the efficiencyof diagnosis. At the same time, it can overcome the misdiagnosis and missed diagnosisthat are bringed about by human eye fatigue and human eye insensitivity to grayscalevalue. It can effective improve the efficiency and accuracy of lung cancer and help doctors to make good decisions.