title: [Detection of lung mini-nodules using multi-feature tracking].
abstract: How to accurately identify mini-nodules in a large amount of high resolution computed tomography (HRCT) images is always a significant and difficult issue in lung nodule computer-aided detection (CAD). This paper describes a new mini-nodules detection method which is based on a multi-feature tracking algorithm. Our detection method began after running the Da-Jing algorithm and morphological operation to extract the lung region of every HRCT image in a sequence. Once the lung had been extracted, a hybrid algorithm, combining gray threshold and improved template matching, was used to obtain the regions of interest (ROD). Next, several characteristics of each ROI were calculated to identify the final results by using multi-feature tracking throughout the whole HRCT image sequence. The results showed that the proposed method would be of high accuracy with a low occurrence of false positives.
authors.affiliation: College of Automation Science and Engineering, South China University of Technology, and Image Center, Clifford Hospital, Guangzhou 510640, China.
authors.forename: Li
authors.initials: L
authors.lastname: Tan
authors.forename: Bin
authors.initials: B
authors.lastname: Li
authors.forename: Lianfang
authors.initials: L
authors.lastname: Tian
authors.forename: Lifei
authors.initials: L
authors.lastname: Wang
authors.forename: Ping
authors.initials: P
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed