|dc.identifier.citation||Nordby, Pål. A combined structural/statistical texture analysis of monolayer ovarian cancer cell nuclei. Masteroppgave, University of Oslo, 2010||en_US
|dc.description.abstract||Determining the prognosis in an early stage of human cancer can be
essential for the choice of optimal therapy. Digital image analysis
of cell nuclei is a very useful tool to obtain quantitative
information for robust and reliable prognosis. A substantial number of
papers have been published on the use of various texture analysis
methods for diagnostic and prognostic work on human cancer, and
most of the studies are based on texture analysis of the gray levels
in the images.
We will take another approach, and use a refined adaptive
segmentation method developed in this thesis to describe the
structures inside the cell nuclei images. The refined thresholding
method is spatially adaptive within each image, while its parameters
are adapted to the histogram of each image. In a novel approach, we
evaluate the characteristics of the segmented structures statistically
to decide the prognosis per image, and finally a rule is formed to
classify each patient.
The data set analyzed consists of 134 patients with early ovarian
cancer. The problems with such small data sets is
addressed, and a solution based on statistical bootstrapping is
proposed. This gives a more robust estimate of the correct
classification rate (CCR) than the traditional single CCR estimate
would, and in addition gives a CCR uncertainty estimate.
Dividing the data set into two groups based on DNA-ploidy - effectively
introducing a simple two-step classification scheme - substantially
improved the performance of the classification. Combining the
structural features extracted from the objects inside each
cell nucleus with the best statistical gray level feature - an
adaptive entropy matrix feature from a previous study on the same
material - further improved the correct classification rate, leading
to a CCR close to 90\%.
In conclusion, the significant improvement in correct classification
rate obtained by combining the best statistical and structural texture
features seems to hold a promise of very high CCRs, which would be
immensely valuable in prognostic work on human cancers. This may be
true beyond the present data set, and possibly quite generally.
But obviously some caution is called for, and more tests on different
and larger data sets should be performed.||eng
|dc.title||A combined structural/statistical texture analysis of monolayer ovarian cancer cell nuclei||en_US
|dc.identifier.bibliographiccitation||info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft.au=Nordby, Pål&rft.title=A combined structural/statistical texture analysis of monolayer ovarian cancer cell nuclei&rft.inst=University of Oslo&rft.date=2010&rft.degree=Masteroppgave||en_US
|dc.contributor.supervisor||Fritz Albregtsen, Håvard E. Danielsen||en_US