Detection of Pulmonary Nodules in CT Images Based on Fuzzy Integrated Active Contour Model and Hybrid Parametric Mixture Model

摘要

The segmentation and detection of various types of nodules in a Computer-aided detection(CAD) system present various challenges, especially when (1) the nodule is connected to a vesseland they have very similar intensities; (2) the nodule with ground-glass opacity (GGO)characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficultto define the boundaries. Traditional segmentation methods may cause problems of boundaryleakage and “weak” local minima. This paper deals with the above mentioned problems. Animproved detection method which combines a fuzzy integrated active contour model(FIACM)-based segmentation method, a segmentation refinement method based on ParametricMixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM(Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types ofpulmonary nodules in computerized tomography (CT) images. Our approach has several novelaspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) Ahybrid PMM Model of juxta-vascular nodules combining appearance and geometricinformation is constructed for segmentation refinement of juxta-vascular nodules. Experimentalresults of detection for pulmonary nodules show desirable performances of the proposedmethod.