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1 March 2008 Computer-aided identification of ovarian cancer in confocal microendoscope images
Saurabh Srivastava, Jeffrey J. Rodríguez, Andrew R. Rouse, Molly A. Brewer, Arthur F. Gmitro
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Journal of Biomedical Optics, Vol. 13, Issue 2, 024021 (March 2008). https://doi.org/10.1117/1.2907167
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.
©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
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Saurabh Srivastava, Jeffrey J. Rodríguez, Andrew R. Rouse, Molly A. Brewer, and Arthur F. Gmitro "Computer-aided identification of ovarian cancer in confocal microendoscope images," Journal of Biomedical Optics 13(2), 024021 (1 March 2008). https://doi.org/10.1117/1.2907167
Published: 1 March 2008
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Cited by 38 scholarly publications and 1 patent.
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KEYWORDS
Confocal microscopy
Tissues
Ovarian cancer
Feature selection
Error analysis
Natural surfaces
Principal component analysis
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