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.
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.
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