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dc.contributor.authorGarcía Allende, Pilar Beatriz
dc.contributor.authorKrishnaswamy, Venkataramanan
dc.contributor.authorSamkoe, Kimberley S.
dc.contributor.authorHoopes, P. Jack
dc.contributor.authorPogue, Brian William
dc.contributor.authorConde Portilla, Olga María 
dc.contributor.authorLópez Higuera, José Miguel 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2013-06-26T06:38:23Z
dc.date.available2013-06-26T06:38:23Z
dc.date.issued2009-02-24
dc.identifier.issn1996-756X
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/10902/2516
dc.description.abstractMulti-spectral scatter visualization of tissue ultra-structure in situ can provide a unique tool for guiding surgical resection, but since changes are subtle and the data is multi-parametric, an automated methodology was sought to interpret these data, in order to classify their tissue sub-type. Tissue types observed across AsPC-1 pancreatic tumor samples were pathologically classified under three major groups (epithelium, fibrosis and necrosis) and the variations in scattering parameters, i.e. scattering power, scattering amplitude and average scattered intensity, across these groups were analyzed. The proposed scheme uses statistical pre-processing of the scattering parameter images to create additional data features followed by a k-nearest neighbors (kNN) based algorithm for tissue type classification. The classification accuracy inside some predefined regions of interest was determined and the mean region values of scattering parameters turned out to be stronger data sets for classification, rather than the individual pixel values. This presumably indicates that pixel-to-pixel variations in the remitted spectra need to be minimized for reliable classification approaches. Results show a strong correlation between the automated and expert-based classification within the predefined regions of interest.es_ES
dc.format.extent9 p.es_ES
dc.language.isoenges_ES
dc.publisherSPIE Society of Photo-Optical Instrumentation Engineerses_ES
dc.rights© 2009 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.es_ES
dc.sourceProceedings of SPI, 2009, vol. 7187, 718717es_ES
dc.sourceBiomedical Applications of Light Scattering III, San José (CA), 2009es_ES
dc.subject.otherAutomatic classificationes_ES
dc.subject.otherTumores_ES
dc.subject.otherNecrosises_ES
dc.subject.otherConfocal reflectance imaginges_ES
dc.subject.otherScatteres_ES
dc.subject.otherFeature extractiones_ES
dc.subject.otherK-nearest neighbors (kNN)es_ES
dc.titleAutomated segmentation based upon remitted scatter spectra from pathologically distinct tumor regionses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.relation.publisherVersionhttp://dx.doi.org/10.1117/12.808322es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1117/12.808322
dc.type.versionpublishedVersiones_ES


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