Image Processing as Applied to Medical Diagnostics

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dc.contributor.authorThomas, Kristine A.
dc.date.accessioned2010-09-15T23:34:12Z
dc.date.available2010-09-15T23:34:12Z
dc.date.issued2010-06
dc.descriptionxi, 56 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number.en_US
dc.description.abstractImage processing is a powerful tool for increasing the reliability and reproducibility of disease diagnostics. In the hands of pathologists, image processing provides quantitative data from histological images which supplement the qualitative data currently used by specialists. This thesis presents a novel method for analyzing digitized images of hematoxylin and eosin (H&E) stained histology slides to detect and quantify inflammatory polymorphonuclear leukocytes to aid in the grading of acute inflammation of the placenta as an example of the use of image processing in aid of diagnostics. Methods presented in this thesis include segmentation, a novel threshold selection technique and shape analysis. The most significant contribution is the automated color threshold selection algorithm for H&E stained histology slides which is the only unsupervised method published to date.en_US
dc.description.sponsorshipCommittee in charge: Dr. John Conery, Chair; Dr. Matthew J. Sottileen_US
dc.identifier.urihttps://hdl.handle.net/1794/10724
dc.language.isoen_USen_US
dc.publisherUniversity of Oregonen_US
dc.relation.ispartofseriesUniversity of Oregon theses, Dept. of Computer and Information Science, M.S., 2010;
dc.subjectMedical diagnostic imaging
dc.subjectDiagnostic imaging
dc.subjectImage processing
dc.titleImage Processing as Applied to Medical Diagnosticsen_US
dc.typeThesisen_US

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