Classification of human carcinoma cells using multispectral imagery
buir.contributor.author | Çetin, A. Enis | |
buir.contributor.orcid | Çetin, A. Enis|0000-0002-3449-1958 | |
dc.citation.epage | 6 | en_US |
dc.citation.spage | 1 | en_US |
dc.citation.volumeNumber | 9791 | en_US |
dc.contributor.author | Çınar, U. | en_US |
dc.contributor.author | Çetin, Y. Y. | en_US |
dc.contributor.author | Çetin-Atalay, R. | en_US |
dc.contributor.author | Çetin, A. Enis | en_US |
dc.coverage.spatial | San Diego, California, United States | en_US |
dc.date.accessioned | 2018-04-12T11:46:30Z | en_US |
dc.date.available | 2018-04-12T11:46:30Z | en_US |
dc.date.issued | 2016 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 27 February - 3 March 2016 | en_US |
dc.description | Conference Name: SPIE Medical Imaging, 2016 | en_US |
dc.description.abstract | In 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.description.provenance | Made available in DSpace on 2018-04-12T11:46:30Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016 | en |
dc.identifier.doi | 10.1117/12.2217022 | en_US |
dc.identifier.issn | 1605-7422 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/37641 | en_US |
dc.language.iso | English | en_US |
dc.publisher | SPIE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1117/12.2217022 | en_US |
dc.source.title | Proceedings of SPIE Vol. 9791, Medical Imaging 2016: Digital Pathology | en_US |
dc.subject | Automatic classification | en_US |
dc.subject | Cancer cells | en_US |
dc.subject | Gabor features | en_US |
dc.subject | Multispectral imaging | en_US |
dc.title | Classification of human carcinoma cells using multispectral imagery | en_US |
dc.type | Conference Paper | en_US |
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