title: The development and evaluation of a computerized diagnosis scheme for pneumoconiosis on digital chest radiographs.
abstract: OBJECTIVE
  To diagnose pneumoconiosis using a computer-aided diagnosis system based on digital chest radiographs.
  
  
  METHODS
  Lung fields were first extracted by combining the traditional Otsu-threshold method with a morphological reconstruction on digital radiographs (DRs), and then subdivided into six non-overlapping regions (region (a-f)). Twenty-two wavelet-based energy texture features were calculated exclusively from each region and selected using a decision tree algorithm. A support vector machine (SVM) with a linear kernel was trained using samples with texture features to classify an individual region of a healthy subject or a pneumoconiosis patient. The final classification results were obtained by integrating these individual classifiers with the weighted voting method. All models were developed on a dataset of 85 healthy controls and 40 stage I or II pneumoconiosis patients and validated by using the bootstrap resampling with replacement method.
  
  
  RESULTS
  The areas under receiver operating characteristic curves (AUCs) of regions (c) and (f) were 0.688 and 0.563, which were worse than those of the other four regions. Region (c) and (f) were both excluded from the individual classifiers that were going to be assembled further. When built on the selected texture features, each individual SVM showed a higher diagnostic performance for the training set and the test set. The classification performance after an ensemble was 0.997 and 0.961 of the AUC value for the training and test sets, respectively. The final results were 0.974 ± 0.018 for AUC value and 0.929 ± 0.018 for accuracy.
  
  
  CONCLUSIONS
  The integrated SVM model built on the selected feature set showed the highest diagnostic performance among all individual SVM models. The model has good potential in diagnosing pneumoconiosis based on digital chest radiographs.
authors.forename: Biyun
authors.initials: B
authors.lastname: Zhu
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.forename: Baoping
authors.initials: B
authors.lastname: Li
authors.forename: Budong
authors.initials: B
authors.lastname: Chen
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Yang
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Wu
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing 100069, China. chenhui@ccmu.edu.cn.
authors.forename: Hui
authors.initials: H
authors.lastname: Chen
authors.forename: Kuan
authors.initials: K
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D016022:Case-Control Studies; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016014:Linear Models; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D011009:Pneumoconiosis / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted; D013902:Radiography, Thoracic / Q000379:methods*; D060388:Support Vector Machine