title: Adhesion Pulmonary Nodules Detection Based on Dot-Filter and Extracting Centerline Algorithm.
abstract: A suspected pulmonary nodule detection method was proposed based on dot-filter and extracting centerline algorithm. In this paper, we focus on the distinguishing adhesion pulmonary nodules attached to vessels in two-dimensional (2D) lung computed tomography (CT) images. Firstly, the dot-filter based on Hessian matrix was constructed to enhance the circular area of the pulmonary CT images, which enhanced the circular suspected pulmonary nodule and suppresses the line-like areas. Secondly, to detect the nondistinguishable attached pulmonary nodules by the dot-filter, an algorithm based on extracting centerline was developed to enhance the circle area formed by the end or head of the vessels including the intersection of the lines. 20 sets of CT images were used in the experiments. In addition, 20 true/false nodules extracted were used to test the function of classifier. The experimental results show that the method based on dot-filter and extracting centerline algorithm can detect the attached pulmonary nodules accurately, which is a basis for further studies on the pulmonary nodule detection and diagnose.
authors.affiliation: College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
authors.forename: Liwei
authors.initials: L
authors.lastname: Liu
authors.affiliation: College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Wang
authors.affiliation: College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.affiliation: Sansom Institute for Health Research and School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA 5001, Australia.
authors.forename: Liping
authors.identifier: 0000-0001-9355-1167
authors.initials: L
authors.lastname: Wang
authors.affiliation: School of Natural and Built Environments, University of South Australia, Adelaide, SA 5095, Australia.
authors.forename: Jianghui
authors.identifier: 0000-0003-3961-1688
authors.initials: J
authors.lastname: Dong
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D015233:Models, Statistical; D055613:Multiple Pulmonary Nodules / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data