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Image Analysis Algorithms for Ovarian Cancer Detection Using Confocal Microendoscopy

Confocal microendoscopy is a promising new diagnostic imaging technique that is minimally invasive and provides in-vivo cellular-level images of tissue. In this study, we developed various image analysis techniques for ovarian cancer detection using the confocal microendoscope system. Firstly, we developed a technique for automatic classification of images based on focus, to prune out the out-of-focus images from the ovarian dataset. Secondly, we modified the texture analysis technique developed earlier to improve the stability of the textural features. The modified technique gives stable features and more consistent performance for ovarian cancer detection. Although confocal microendoscopy provides cellular-level resolution, it is limited by a small field of view. We present a fast technique for stitching the individual frames of the tissue to form a large mosaic. Such a mosaic will aid the physician in diagnosis, and also makes quantitative and statistical analysis possible on a larger field of view.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/193311
Date January 2008
CreatorsPatel, Mehul Bhupendra
ContributorsGmitro, Arthur F., Rodriguez, Jeffrey J., Rodriguez, Jeffrey J., Marcellin, Michael, Gmitro, Arthur F.
PublisherThe University of Arizona.
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
Typetext, Electronic Thesis
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

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