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23 March 2016 Classification of human carcinoma cells using multispectral imagery
Umut Çinar, Yasemin Y. Çetin, Rengul Çetin-Atalay, Enis Çetin
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Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 97911C (2016) https://doi.org/10.1117/12.2217022
Event: SPIE Medical Imaging, 2016, San Diego, California, United States
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.
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Umut Çinar, Yasemin Y. Çetin, Rengul Çetin-Atalay, and Enis Çetin "Classification of human carcinoma cells using multispectral imagery", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97911C (23 March 2016); https://doi.org/10.1117/12.2217022
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KEYWORDS
Microscopy
Image classification
RGB color model
Image segmentation
Multispectral imaging
Breast
Cancer
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