title: Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier.
abstract: Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.
authors.affiliation: School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Gong
authors.affiliation: School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
authors.forename: Ji-Yu
authors.initials: JY
authors.lastname: Liu
authors.affiliation: School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
authors.forename: Li-Jia
authors.initials: LJ
authors.lastname: Wang
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
authors.affiliation: School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. Electronic address: nsd4647@163.com.
authors.forename: Sheng-Dong
authors.initials: SD
authors.lastname: Nie
mesh_terms: D016002:Discriminant Analysis; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed
keywords: CT images; Computer-aided detection; FLDA; Pulmonary nodule; Template matching