title: Computer-aided detection of lung nodules by SVM based on 3D matrix patterns.
abstract: OBJECTIVE
  The objective was to prevent loss of some implicit structural and local contextual information of lung nodules by current one- (1D) or two-dimensional (2D) schemes.
  
  
  METHODS
  The testing data set used in this study consisted of computed tomographic scans from 196 different patients in Jilin Tumor Hospital, which consisted of 8428 sections including 108 nodules. By the proposed support vector machine based on three dimensional matrix patterns (SVM(3Dmatrix)) which improves the classifier of SVM, 3D volume of interest of suspected lung nodules can be used directly as the training samples. The 3D scheme may effectively reduce the large numbers of false positives (FPs) by current 1D and 2D schemes.
  
  
  RESULTS
  Five computer-aided diagnosis (CAD) schemes were investigated for the same 196-case database. SVM(3Dmatrix) achieved a 98.2% overall sensitivity with 9.1 FPs per section, which was in general superior compared to the other four CAD schemes for our application.
authors.affiliation: School of Information Engineering, Northeast Dianli University, Jilin 132012, China. wangqingzhu198339@163.com
authors.forename: Qingzhu
authors.initials: Q
authors.lastname: Wang
authors.forename: Wenwei
authors.initials: W
authors.lastname: Kang
authors.forename: Chunming
authors.initials: C
authors.lastname: Wu
authors.forename: Bin
authors.initials: B
authors.lastname: Wang
mesh_terms: D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000379:methods*