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Proceedings Paper

Classification of human carcinoma cells using multispectral imagery
Author(s): Umut Çinar; Yasemin Y. Çetin; Rengul Çetin-Atalay; Enis Çetin
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Paper 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.

Paper Details

Date Published: 23 March 2016
PDF: 6 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97911C (23 March 2016); doi: 10.1117/12.2217022
Show Author Affiliations
Umut Çinar, Middle East Technical Univ. (Turkey)
Yasemin Y. Çetin, Middle East Technical Univ. (Turkey)
Rengul Çetin-Atalay, Middle East Technical Univ. (Turkey)
Enis Çetin, Bilkent Univ. (Turkey)


Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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