Classification of human carcinoma cells using multispectral imagery

Umut Çinar; Yasemin Y. Çetin; Rengul Çetin-Atalay; Enis Çetin

doi:10.1117/12.2217022

23 March 2016 Classification of human carcinoma cells using multispectral imagery

Umut Çinar, Yasemin Y. Çetin, Rengul Çetin-Atalay, Enis Çetin

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.

Citation Download Citation

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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