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dc.contributor.authorEguizabal Aguado, Alma 
dc.contributor.authorLaughney, Ashley M.
dc.contributor.authorGarcía Allende, Pilar Beatriz
dc.contributor.authorKrishnaswamy, Venkataramanan
dc.contributor.authorWells, Wendy A.
dc.contributor.authorPaulsen, Keith D.
dc.contributor.authorPogue, Brian William
dc.contributor.authorLópez Higuera, José Miguel 
dc.contributor.authorConde Portilla, Olga María 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2013-06-26T06:32:53Z
dc.date.available2013-06-26T06:32:53Z
dc.date.issued2013-02-21
dc.identifier.issn1996-756X
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/10902/2514
dc.description.abstractTexture analysis of light scattering in tissue is proposed to obtain diagnostic information from breast cancer specimens. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology to guide the surgeon in resection surgeries. The usability of scatter signatures acquired with a micro-sampling reflectance spectral imaging system was improved utilizing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. Co-occurrence analysis is then applied to the scattering power images to extract the textural features. A statistical analysis of the features demonstrated the suitability of the autocorrelation for the classification of notmalignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue, since it reveals morphological information of tissue. Non-malignant tissue shows higher autocorrelation values while adipose tissue presents a very low autocorrelation on its scatter texture, being malignant the middle ground. Consequently, a fast linear classifier based on the consideration of just one straightforward feature is enough for providing relevant diagnostic information. A leave-one-out validation of the linear classifier on 29 samples with 48 regions of interest showed classification accuracies of 98.74% on adipose tissue, 82.67% on non-malignant tissue and 72.37% on malignant tissue, in comparison with the biopsy H and E gold standard. This demonstrates that autocorrelation analysis of scatter signatures is a very computationally efficient and automated approach to provide pathological information in real-time to guide surgeon during tissue resection.es_ES
dc.format.extent8 p.es_ES
dc.language.isoenges_ES
dc.publisherSPIE Society of Photo-Optical Instrumentation Engineerses_ES
dc.rights© 2013 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 SPIE, 2013, vol. 8592, 85920Ees_ES
dc.sourceBiomedical Applications of Light Scattering VII, San Francisco (CA), 2013es_ES
dc.subject.otherBreast tumores_ES
dc.subject.otherLocalized backscatteringes_ES
dc.subject.otherScattering poweres_ES
dc.subject.otherTexture analysises_ES
dc.subject.otherLinear classifieres_ES
dc.titleLinear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extractiones_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.relation.publisherVersionhttp://dx.doi.org/10.1117/12.2003814es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1117/12.2003814
dc.type.versionpublishedVersiones_ES


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