Research Papers

Computer-aided identification of ovarian cancer in confocal microendoscope images

[+] Author Affiliations
Saurabh Srivastava

University of Arizona, Department of Electrical & Computer Engineering, 360 W. 34th St., Apt. K, New York, New York 10001 ss2922@columbia.edu

Jeffrey J. Rodríguez

University of Arizona, Department of Electrical & Computer Engineering, 1230 E. Speedway Blvd., P.O. Box 210104, Tucson, Arizona 85721

Andrew R. Rouse

University of Arizona, Department of Radiology, Radiology Research Laboratory, P.O. 245067, Tucson, Arizona 85724

Molly A. Brewer

University of Arizona, Department of Obstetrics & Gynecology, Arizona Cancer Center, Rm. 1968G, P.O. Box 245024, 1515 N. Campbell Avenue, Tucson, Arizona 85724

Arthur F. Gmitro

University of Arizona, Department of Radiology and Optical Sciences, Radiology Research Laboratory, P.O. 245067, Tucson, Arizona 85724

J. Biomed. Opt. 13(2), 024021 (May 02, 2008). doi:10.1117/1.2907167
History: Received July 06, 2007; Revised November 02, 2007; Accepted November 06, 2007; Published May 02, 2008
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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.

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© 2008 Society of Photo-Optical Instrumentation Engineers

Citation

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", J. Biomed. Opt. 13(2), 024021 (May 02, 2008). ; http://dx.doi.org/10.1117/1.2907167


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