A study on diagnostic image analysis for the detection of precancerous lesions using multi-spectral digital images

dc.contributor.advisorRichards-Kortum, Rebecca, 1964-en
dc.contributor.advisorMarkey, Mia Kathleenen
dc.creatorPark, Sun Young, 1972-en
dc.date.accessioned2008-08-28T23:36:18Zen
dc.date.available2008-08-28T23:36:18Zen
dc.date.issued2007en
dc.descriptiontexten
dc.description.abstractThis dissertation explores a diagnostic image analysis framework using multispectral digital colposcopy for real-time in vivo detection of cervical cancer. In the first part of the dissertation, the clinical feasibility of a previously developed multispectral digital colposcope (MDC) is demonstrated using a hamster cheek pouch model of carcinogenesis. Various studies on MDC applications to cervical cancer detection in human subjects are then presented. First, an automated diagnostic image analysis algorithm for cervical cancer using white light reflectance images is presented. The algorithm can identify pre-neoplastic tissue areas from an entire cervix based on intensity changes feature in the reflectance images induced by acetic acid treatment. Then, the information about tissue type is incorporated into the diagnostic image analysis framework. For this purpose, a Markov Random Field (MRF) model is adopted and the results are discussed. One of the practical difficulties of utilizing a MRF model in unpolarized white light reflectance imaging is the specular reflection problem since the effect of specular reflection extends into surrounding tissue areas. Through the use of cross polarized imaging, the effects of specular reflection reduced and the ability to segment images based on tissue types is enhanced, leading to better diagnostic performance. The diagnostic performance of polarized imaging is compared to that of unpolarized imaging. In order to assess the performance of the proposed approach, a gold standard for the entire cervical image is constructed using histopathology results from a whole cervix specimen. The results presented in this dissertation indicate that an automated diagnostic image analysis framework for early detection of cervical cancer has the potential to be clinically applied as a low cost alternative screening technique in developing countries. Advances in imaging technology as well as in image analysis algorithms will continue to reduce the cost of diagnostic imaging systems and improve the imaging and diagnostic capability, leading to an inexpensive, real-time, minimally-invasive alternative to conventional screening techniques for early detection of cervical cancer in developing countries.en
dc.description.departmentBiomedical Engineeringen
dc.format.mediumelectronicen
dc.identifierb68890096en
dc.identifier.oclc174114096en
dc.identifier.urihttp://hdl.handle.net/2152/3243en
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.subject.lcshCervix uteri--Cancer--Diagnosisen
dc.titleA study on diagnostic image analysis for the detection of precancerous lesions using multi-spectral digital imagesen
dc.type.genreThesisen
thesis.degree.departmentBiomedical Engineeringen
thesis.degree.disciplineBiomedical Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen

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