Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung

Med Phys. 2006 Jul;33(7):2610-20. doi: 10.1118/1.2207131.


An automatic method for textural analysis of complete HRCT lung slices is presented. The system performs classification of regions of interest (ROIs) into one of six classes: normal, hyperlucency, fibrosis, ground glass, solid, and focal. We propose a novel method of automatically generating ROIs that contain homogeneous texture. The use of such regions rather than square regions is shown to improve performance of the automated system. Furthermore, the use of two different, previously published, feature sets is investigated. Both feature sets are shown to yield similar results. Classification performance of the complete system is characterized by ROC curves for each of the classes of abnormality and compared to a total of three expert readings by two experienced radiologists. The different types of abnormality can be automatically distinguished with areas under the ROC curve that range from 0.74 (focal) to 0.95 (solid). The kappa statistics for intraobserver agreement, interobserver agreement, and computer versus observer agreement were 0.70, 0.53+/-0.02, and 0.40+/-0.03, respectively. The question whether or not a class of abnormality was present in a slice could be answered by the computer system with an accuracy comparable to that of radiologists.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Automation
  • Data Interpretation, Statistical
  • Diagnosis, Computer-Assisted
  • Humans
  • Lung / diagnostic imaging*
  • Lung / pathology
  • Lung Neoplasms / classification
  • Lung Neoplasms / diagnosis*
  • Lung Neoplasms / diagnostic imaging*
  • Models, Statistical
  • Pattern Recognition, Automated
  • ROC Curve
  • Software
  • Tomography, X-Ray Computed / methods*