Automated texture-based 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 journalConference articlepeer-review

7 Scopus citations


The fluorescence confocal microendoscope provides high-resolution, in-vivo imaging of cellular pathology during optical biopsy. There are indications that the examination of human ovaries with this instrument has diagnostic implications for the early detection of ovarian cancer. The purpose of this study was to develop a computer-aided system to facilitate the identification of ovarian cancer from digital images captured with the confocal microendoscope system. To achieve this goal, we modeled the cellular-level structure present in these images as texture and extracted features based on first-order statistics, spatial gray-level dependence matrices, and spatial-frequency content. Selection of the best features for classification was performed using traditional feature selection techniques including stepwise discriminant analysis, forward sequential search, a non-parametric method, principal component analysis, and a heuristic technique that combines the results of these methods. The best set of features selected was used for classification, and performance of various machine classifiers was compared by analyzing the areas under their receiver operating characteristic curves. The results show that it is possible to automatically identify patients with ovarian cancer based on texture features extracted from confocal microendoscope images and that the machine performance is superior to that of the human observer.

Original languageEnglish (US)
Article number07
Pages (from-to)42-52
Number of pages11
JournalProgress in Biomedical Optics and Imaging - Proceedings of SPIE
StatePublished - 2005
EventThree- Dimensional and Multidomensional Microscopy: Image Acquisition and Processing XII - San Jose, CA, United States
Duration: Jan 25 2005Jan 27 2005


  • Automated classification
  • Confocal microendoscope
  • Ovarian cancer
  • Pattern recognition
  • Texture analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging
  • Biomaterials


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