title: Pulmonary CT image classification with evolutionary programming.
abstract: OBJECTIVE
  It is often difficult to classify information in medical images from derived features. The purpose of this research was to investigate the use of evolutionary programming as a tool for selecting important features and generating algorithms to classify computed tomographic (CT) images of the lung.
  
  
  METHODS
  Training and test sets consisting of 11 features derived from multiple lung CT images were generated, along with an indicator of the target area from which features originated. The images included five parameters based on histogram analysis, 11 parameters based on run length and co-occurrence matrix measures, and the fractal dimension. Two classification experiments were performed. In the first, the classification task was to distinguish between the subtle but known differences between anterior and posterior portions of transverse lung CT sections. The second classification task was to distinguish normal lung CT images from emphysematous images. The performance of the evolutionary programming approach was compared with that of three statistical classifiers that used the same training and test sets.
  
  
  RESULTS
  Evolutionary programming produced solutions that compared favorably with those of the statistical classifiers. In separating the anterior from the posterior lung sections, the evolutionary programming results were better than two of the three statistical approaches. The evolutionary programming approach correctly identified all the normal and abnormal lung images and accomplished this by using less features than the best statistical method.
  
  
  CONCLUSIONS
  The results of this study demonstrate the utility of evolutionary programming as a tool for developing classification algorithms.
authors.affiliation: Department of Radiology, University of Iowa, Iowa City 52242, USA.
authors.forename: M T
authors.initials: MT
authors.lastname: Madsen
authors.forename: R
authors.initials: R
authors.lastname: Uppaluri
authors.forename: E A
authors.initials: EA
authors.lastname: Hoffman
authors.forename: G
authors.initials: G
authors.lastname: McLennan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008168:Lung / Q000000981:diagnostic imaging*; D011656:Pulmonary Emphysema / Q000145:classification / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed