Image Analysis Algorithms for Ovarian Cancer Detection Using Confocal Microendoscopy

Persistent Link:
http://hdl.handle.net/10150/193311
Title:
Image Analysis Algorithms for Ovarian Cancer Detection Using Confocal Microendoscopy
Author:
Patel, Mehul Bhupendra
Issue Date:
2008
Publisher:
The University of Arizona.
Rights:
Copyright © 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.
Abstract:
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.
Type:
text; Electronic Thesis
Keywords:
texture analysis; focus detection; image mosaicing; fast image registration; co-occurrence matrix
Degree Name:
MS
Degree Level:
masters
Degree Program:
Electrical & Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Gmitro, Arthur F.; Rodriguez, Jeffrey J.
Committee Chair:
Rodriguez, Jeffrey J.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleImage Analysis Algorithms for Ovarian Cancer Detection Using Confocal Microendoscopyen_US
dc.creatorPatel, Mehul Bhupendraen_US
dc.contributor.authorPatel, Mehul Bhupendraen_US
dc.date.issued2008en_US
dc.publisherThe University of Arizona.en_US
dc.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.en_US
dc.description.abstractConfocal 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.en_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
dc.subjecttexture analysisen_US
dc.subjectfocus detectionen_US
dc.subjectimage mosaicingen_US
dc.subjectfast image registrationen_US
dc.subjectco-occurrence matrixen_US
thesis.degree.nameMSen_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGmitro, Arthur F.en_US
dc.contributor.advisorRodriguez, Jeffrey J.en_US
dc.contributor.chairRodriguez, Jeffrey J.en_US
dc.contributor.committeememberMarcellin, Michaelen_US
dc.contributor.committeememberGmitro, Arthur F.en_US
dc.identifier.proquest2566en_US
dc.identifier.oclc659748500en_US
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