Paper
24 March 2005 Automated texture-based identification of ovarian cancer in confocal microendoscope images
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Abstract
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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Saurabh Srivastava, Jeffrey J. Rodriguez, Andrew R. Rouse, Molly A. Brewer, and Arthur F. Gmitro "Automated texture-based identification of ovarian cancer in confocal microendoscope images", Proc. SPIE 5701, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XII, (24 March 2005); https://doi.org/10.1117/12.590592
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Cited by 8 scholarly publications.
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KEYWORDS
Confocal microscopy

Tissues

Ovarian cancer

Feature selection

Biopsy

Ovary

Principal component analysis

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