title: Computer-aided identification of ovarian cancer in confocal microendoscope images.
abstract: 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.
authors.affiliation: University of Arizona, Department of Electrical & Computer Engineering, 360 W. 34th St., Apt. K, New York, New York 10001, USA. ss2922@columbia.edu
authors.forename: Saurabh
authors.initials: S
authors.lastname: Srivastava
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Rodríguez
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Rouse
authors.forename: Molly A
authors.initials: MA
authors.lastname: Brewer
authors.forename: Arthur F
authors.initials: AF
authors.lastname: Gmitro
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004724:Endoscopy / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D010051:Ovarian Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity