title: Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model.
abstract: 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 vessel and 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 difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and "weak" local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (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) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method.
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangdong, Guangzhou 510640, China. binlee@scut.edu.cn
authors.forename: Bin
authors.initials: B
authors.lastname: Li
authors.forename: Kan
authors.initials: K
authors.lastname: Chen
authors.forename: Lianfang
authors.initials: L
authors.lastname: Tian
authors.forename: Yao
authors.initials: Y
authors.lastname: Yeboah
authors.forename: Shanxing
authors.initials: S
authors.lastname: Ou
mesh_terms: D019295:Computational Biology; D016208:Databases, Factual; D005188:False Negative Reactions; D005189:False Positive Reactions; D017143:Fuzzy Logic; D006801:Humans; D051188:Knowledge Bases; D008175:Lung Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D003074:Solitary Pulmonary Nodule / Q000098:blood supply / Q000000981:diagnostic imaging*; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*