Research On Detection Of Pulmonary Nodules Based On Lung Images Classification

Because of circumstance pollution, the incidence of pulmonary disease is increasing obviously. More and more persons pay attention to their lung health. As an important means of detecting lung disease, CT has been applied in clinic. For the diagnosis of lung disease, identifying pulmonary nodules is very important.In this thesis, detection of pulmonary nodules based on lung CT images is studied. Firstly, the lung segmentation is segmented, in which some suspicious regions are marked. Then the features of the regions are extracted which are used for classification. Finally, the pulmonary nodules will be detected.In this thesis, with the advantages of Level Set, a new method based on morphology and Level Set is proposed for lung segmentation. The most important character of this method is that methods of morphology is used to gain narrowband of contour and the initialization contour of lung, making Level Set function evolution only in the narrowband. The results of experimentation show that with this method performance of segmentation is improved obviously.For feature extraction, since the quality of feature extraction is a crucial element of the performance of classification, choosing a proper way to extract features of image is extremely important. There are a lot of ways to describe image features, such as color, texture, and shape. Symptoms of pulmonary nodule in clinical diagnosis are further researched, and 29 different features of pulmonary nodules are determined by use of outline analysis and image analysis.For classification of pulmonary nodules, supported vector machine (SVM) is imported, which is a machine learning method. Firstly, the theoretical principle and mathematical model of SVM, especially the popularizing ability and kernel function theory are analyzed. Then the feature extraction results are taken as input and SVM is applied to classify pulmonary nodules.The experimental results show that the method in this thesis can be regarded as a technique for CAD systems in CT pulmonary nodules.