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dc.contributor.authorSawyer, Travis W
dc.contributor.authorChandra, Swati
dc.contributor.authorRice, Photini F S
dc.contributor.authorKoevary, Jennifer W
dc.contributor.authorBarton, Jennifer K
dc.date.accessioned2019-02-22T23:01:45Z
dc.date.available2019-02-22T23:01:45Z
dc.date.issued2018-12
dc.identifier.citationTravis W Sawyer et al 2018 Phys. Med. Biol. 63 235020en_US
dc.identifier.issn1361-6560
dc.identifier.pmid30511664
dc.identifier.doi10.1088/1361-6560/aaefd2
dc.identifier.urihttp://hdl.handle.net/10150/631741
dc.description.abstractOvarian cancer has the lowest survival rate among all gynecologic cancers due to predominantly late diagnosis. Optical coherence tomography (OCT) has been applied successfully to experimentally image the ovaries in vivo; however, a robust method for analysis is still required to provide quantitative diagnostic information. Recently, texture analysis has proved to be a useful tool for tissue characterization; unfortunately, existing work in the scope of OCT ovarian imaging is limited to only analyzing 2D sub-regions of the image data, discarding information encoded in the full image area, as well as in the depth dimension. Here we address these challenges by testing three implementations of texture analysis for the ability to classify tissue type. First, we test the traditional case of extracted 2D regions of interest; then we extend this to include the entire image area by segmenting the organ from the background. Finally, we conduct a full volumetric analysis of the image volume using 3D segmented data. For each case, we compute features based on the Grey-Level Co-occurence Matrix and also by introducing a new approach that evaluates the frequency distribution in the image by computing the energy density. We test these methods on a mouse model of ovarian cancer to differentiate between age, genotype, and treatment. The results show that the 3D application of texture analysis is most effective for differentiating tissue types, yielding an average classification accuracy of 78.6%. This is followed by the analysis in 2D with the segmented image volume, yielding an average accuracy of 71.5%. Both of these improve on the traditional approach of extracting square regions of interest, which yield an average classification accuracy of 67.7%. Thus, applying texture analysis in 3D with a fully segmented image volume is the most robust approach to quantitatively characterizing ovarian tissue.en_US
dc.description.sponsorshipNational Science Foundation Graduate Research Fellowship Program [DGE-1143953]; National Institutes of Health under National Cancer Institute [1R01CA195723]; University of Arizona Cancer Center [3P30CA023074]en_US
dc.language.isoenen_US
dc.publisherIOP PUBLISHING LTDen_US
dc.relation.urlhttp://stacks.iop.org/0031-9155/63/i=23/a=235020?key=crossref.8ae01ac485d7923e40b71403663d8a5ben_US
dc.rights© 2018 Institute of Physics and Engineering in Medicine.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectoptical coherence tomographyen_US
dc.subjecttexture analysisen_US
dc.subjectovarian canceren_US
dc.titleThree-dimensional texture analysis of optical coherence tomography images of ovarian tissueen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Coll Opt Scien_US
dc.contributor.departmentUniv Arizona, Dept Biomed Engnen_US
dc.identifier.journalPHYSICS IN MEDICINE AND BIOLOGYen_US
dc.description.note12 month embargo; published online: 4 December 2018en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitlePhysics in Medicine & Biology
dc.source.volume63
dc.source.issue23
dc.source.beginpage235020


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