Classification of human carcinoma cells using multispectral imagery

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage6en_US
dc.citation.spage1en_US
dc.citation.volumeNumber9791en_US
dc.contributor.authorÇınar, U.en_US
dc.contributor.authorÇetin, Y. Y.en_US
dc.contributor.authorÇetin-Atalay, R.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialSan Diego, California, United Statesen_US
dc.date.accessioned2018-04-12T11:46:30Zen_US
dc.date.available2018-04-12T11:46:30Zen_US
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 27 February - 3 March 2016en_US
dc.descriptionConference Name: SPIE Medical Imaging, 2016en_US
dc.description.abstractIn this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options.en_US
dc.identifier.doi10.1117/12.2217022en_US
dc.identifier.issn1605-7422en_US
dc.identifier.urihttp://hdl.handle.net/11693/37641en_US
dc.language.isoEnglishen_US
dc.publisherSPIEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1117/12.2217022en_US
dc.source.titleProceedings of SPIE Vol. 9791, Medical Imaging 2016: Digital Pathologyen_US
dc.subjectAutomatic classificationen_US
dc.subjectCancer cellsen_US
dc.subjectGabor featuresen_US
dc.subjectMultispectral imagingen_US
dc.titleClassification of human carcinoma cells using multispectral imageryen_US
dc.typeConference Paperen_US

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