Çınar, U.Çetin, Y. Y.Çetin-Atalay, R.Çetin, A. Enis2018-04-122018-04-1220161605-7422http://hdl.handle.net/11693/37641Date of Conference: 27 February - 3 March 2016Conference Name: SPIE Medical Imaging, 2016In 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.EnglishAutomatic classificationCancer cellsGabor featuresMultispectral imagingConference Paper10.1117/12.2217022