Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification

IEEE Trans Med Imaging. 2006 May;25(5):602-11. doi: 10.1109/TMI.2006.872747.


The task of segmenting the posterior ribs within the lung fields of standard posteroanterior chest radiographs is considered. To this end, an iterative, pixel-based, supervised, statistical classification method is used, which is called iterated contextual pixel classification (ICPC). Starting from an initial rib segmentation obtained from pixel classification, ICPC updates it by reclassifying every pixel, based on the original features and, additionally, class label information of pixels in the neighborhood of the pixel to be reclassified. The method is evaluated on 30 radiographs taken from the JSRT (Japanese Society of Radiological Technology) database. All posterior ribs within the lung fields in these images have been traced manually by two observers. The first observer's segmentations are set as the gold standard; ICPC is trained using these segmentations. In a sixfold cross-validation experiment, ICPC achieves a classification accuracy of 0.86 +/- 0.06, as compared to 0.94 +/- 0.02 for the second human observer.

Publication types

  • Evaluation Study

MeSH terms

  • Algorithms
  • Artificial Intelligence
  • Humans
  • Information Storage and Retrieval / methods
  • Lung Neoplasms / diagnostic imaging*
  • Observer Variation
  • Pattern Recognition, Automated / methods*
  • Radiographic Image Enhancement / methods*
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Radiography, Thoracic / methods*
  • Reproducibility of Results
  • Retrospective Studies
  • Ribs / diagnostic imaging*
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted*