xi, 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. / Image 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. / Committee in charge:
Dr. John Conery, Chair;
Dr. Matthew J. Sottile
| Identifer | oai:union.ndltd.org:uoregon.edu/oai:scholarsbank.uoregon.edu:1794/10724 |
| Date | 06 1900 |
| Creators | Thomas, Kristine A. |
| Publisher | University of Oregon |
| Source Sets | University of Oregon |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Relation | University of Oregon theses, Dept. of Computer and Information Science, M.S., 2010; |
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