Computer-aided identification of ovarian cancer in confocal microendoscope images

Saurabh Srivastava, Jeffrey J. Rodríguez, Andrew R. Rouse, Molly A. Brewer, Arthur F. Gmitro

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


The confocal microendoscope is an instrument for imaging the surface of the human ovary. Images taken with this instrument from normal and diseased tissue show significant differences in cellular distribution. A real-time computer-aided system to facilitate the identification of ovarian cancer is introduced. The cellular-level structure present in ex vivo confocal microendoscope images is modeled as texture. Features are extracted based on first-order statistics, spatial gray-level-dependence matrices, and spatial-frequency content. Selection of the features is performed using stepwise discriminant analysis, forward sequential search, a nonparametric method, principal component analysis, and a heuristic technique that combines the results of these other methods. The selected features are used for classification, and the performance of various machine classifiers is compared by analyzing areas under their receiver operating characteristic curves. The machine classifiers studied included linear discriminant analysis, quadratic discriminant analysis, and the k-nearest-neighbor algorithm. The results suggest it is possible to automatically identify pathology based on texture features extracted from confocal microendoscope images and that the machine performance is superior to that of a human observer.

Original languageEnglish (US)
Article number024021
JournalJournal of biomedical optics
Issue number2
StatePublished - 2008


  • confocal optics
  • endoscopy
  • image analysis
  • image evaluation
  • pattern recognition

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering


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