Research Of 3D ROI Segmentation, Feature Extraction And Classification Methods For Pulmonary CAD

Lung cancer is one of the most deadly diseases in the world, it threatens people's lives. To improve lung cancer patients'viability, the earliest diagnosis and therapy of lung cancer is essential. CT scanning has been the most important method of lung cancer examination. The lung cancer is represented with the pulmonary nodules in CT images, and since the complexity of the lung structure and the multiplicity of nodule's shape, even the seasoned doctor cannot easily discover all of the possible disease in time. However, with the advent of the multislice CT, the image data which were handled by the physician drastically increased. An assistance is cried for in order to lighten physician's burdens, so computer-aided diagnosis (CAD) is developed gradually. It may help doctors to analyze pathological changes and other regions of interest in character and even in accurate quantity, and release the doctors'burden.In this thesis,3D ROI segmentation, feature extraction and classification methods for pulmonary CAD are researched. The main work includes the following three parts:first,3D segmentation algorithms of region of interest are researched. The whole ROI can be extracted by the proposed adaptive three-dimensional region growth algorithm and the algorithm based on three-dimensional Hessian filter. The results of two algorithms are analysed. Second, a scheme for three-dimensional feature extraction is presented. In the aspect of the spatial context features, six features which consisted of surface, volume, sphericity, convexity, compactness, mean are extracted; in the aspect of the local features, the volumetric shape index features are extracted. These extracted features are identified and evaluated according to medical symptoms. Last, the fisher classifier and the kernel-fisher classifier are designed to distinguish nodules from normal areas after the effective features extraction. The experiment results indicate that the performance of modified kernel-fisher is better than that of the fisher classifier.On the whole, the algorithms put forward in this thesis for ROI segmentation, feature extraction and classification by use of three-dimensional methods have good detected result according to the spatial context messages. And it could be used as supplementary information for the diagnosis of the pulmonary nodules.