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dc.date.accessioned2013-03-12T08:11:11Z
dc.date.available2013-03-12T08:11:11Z
dc.date.issued2010en_US
dc.date.submitted2010-03-03en_US
dc.identifier.citationSørensen, Håvard Frenning. Prognostic Classification of Ovarian Cancer by LBP Texture Analysis of Microscopy Images. Masteroppgave, University of Oslo, 2010en_US
dc.identifier.urihttp://hdl.handle.net/10852/10153
dc.description.abstractFinding useful texture based features is often hard, even if there are clear patterns in the material at hand, but it will be a very useful contribution to any classification process as the texture might be uncorrelated to other more easily found features. So even if the classification rates can not compete with the ones accomplished with other approaches, they might give valuable new information. This thesis looks at the use of the Local Binary Pattern (LBP) texture analysis tech- nique in Prognostic Classification of Ovarian Cancer based on microscopy images of cell nuclei. The textures in this data set do not contain information that makes it possible to classify by human visual inspection. The approach used is not specific to the present data set, but could also be used on most other texture classification problems. Differences between LBP and Gray Level Cooccurrence Matrixes (GLCM) are de- scribed, feature selection in general is discussed, and pseudocode for some of the feature selection algorithms is presented. Extensions to regular LBP are proposed and tested together with the regular version. Grouping of the LBP data based on the nuclei sizes and gray levels and dividing into new features is tested. Other possible approaches are also outlined. The resulting classification rates are low. The reason for this is discussed and the main theory is that too much data is included in each feature. A version of regular LBP that only looks at dark areas is tested and shows some promising results. LBP looks like a promising tool for the future, but this thesis should give a good indication of what problems to be aware of. Any future success using LBP on this data set will probably depend on finding the areas whitin nuclei which have patterns that differ between the prognostic groups, as there does not seem to be any pattern present throughout most of the nuclei.eng
dc.language.isoengen_US
dc.titlePrognostic Classification of Ovarian Cancer by LBP Texture Analysis of Microscopy Imagesen_US
dc.typeMaster thesisen_US
dc.date.updated2010-08-10en_US
dc.creator.authorSørensen, Håvard Frenningen_US
dc.subject.nsiVDP::420en_US
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft.au=Sørensen, Håvard Frenning&rft.title=Prognostic Classification of Ovarian Cancer by LBP Texture Analysis of Microscopy Images&rft.inst=University of Oslo&rft.date=2010&rft.degree=Masteroppgaveen_US
dc.identifier.urnURN:NBN:no-24752en_US
dc.type.documentMasteroppgaveen_US
dc.identifier.duo99714en_US
dc.contributor.supervisorFritz Albregtsenen_US
dc.identifier.bibsys101660162en_US
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/10153/2/Sorensen.pdf


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