title: Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering.
abstract: BACKGROUND
  Pulmonary nodules in computerized tomography (CT) images are potential manifestations of lung cancer. Segmentation of potential nodule objects is the first necessary and crucial step in computer-aided detection system of pulmonary nodules. The segmentation of various types of nodules, especially for ground-glass opacity (GGO) nodules and juxta-vascular nodules, present various challenges. The nodule with GGO characteristic possesses typical intensity inhomogeneity and weak edges, which is difficult to define the boundary; the juxta-vascular nodule is connected to a vessel, and they have very similar intensities. Traditional segmentation methods may result in the problems of boundary leakage and a small volume over-segmentation. This paper deals with the above mentioned problems.
  
  
  METHODS
  A novel segmentation method for pulmonary nodules is proposed, which uses an adaptive local region energy model with probability density function (PDF)-based similarity distance and multi-features dynamic clustering refinement method. Our approach has several novel aspects: (1) in the proposed adaptive local region energy model, the local domain for local energy model is selected adaptively based on k-nearest-neighbour (KNN) estimate method, and measurable distances between probability density functions of multi-dimension features with high class separability are used to build the cost function. (2) A multi-features dynamic clustering method is used for the segmentation refinement of juxta-vascular nodules, which is based on the nodule segmentation using active contour model (ACM) with adaptive local region energy and vessel segmentation using flow direction feature (FDF)-based region growing method. (3) it handles various types of nodules under a united framework.
  
  
  RESULTS
  The proposed method has been validated on a clinical dataset of 113 chest CT scans that contain 157 nodules determined by a ground truth reading process, and evaluating the algorithm on the provided data leads to an average Tanimoto/Jaccard error of 0.17, 0.20 and 0.24 for GGO, juxta-vascular and GGO juxta-vascular nodules, respectively.
  
  
  CONCLUSIONS
  Experimental results show desirable performances of the proposed method. The proposed segmentation method outperforms the traditional methods.
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, Guangdong, China. binlee@scut.edu.cn.
authors.forename: Bin
authors.initials: B
authors.lastname: Li
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, Guangdong, China.
authors.forename: QingLin
authors.initials: Q
authors.lastname: Chen
authors.affiliation: Department of Radiology, Guangzhou General Hospital of Guangzhou Command, Guangzhou, 510010, Guangdong, China.
authors.forename: Guangming
authors.initials: G
authors.lastname: Peng
authors.affiliation: Department of Radiology, Guangzhou General Hospital of Guangzhou Command, Guangzhou, 510010, Guangdong, China.
authors.forename: Yuanxing
authors.initials: Y
authors.lastname: Guo
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, Guangdong, China.
authors.forename: Kan
authors.initials: K
authors.lastname: Chen
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, Guangdong, China.
authors.forename: LianFang
authors.initials: L
authors.lastname: Tian
authors.affiliation: Department of Radiology, Guangzhou General Hospital of Guangzhou Command, Guangzhou, 510010, Guangdong, China.
authors.forename: Shanxing
authors.initials: S
authors.lastname: Ou
authors.affiliation: Department of Radiology, Shenzhen Third People's Hospital, Shenzhen, 518112, Guangdong, China.
authors.forename: Lifei
authors.initials: L
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed
keywords: Active contour model; Computer tomography image; Feature extraction; Image segmentation; Pulmonary nodules