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Proceedings Paper

Quantitative diagnosis of tongue cancer from histological images in an animal model
Author(s): Guolan Lu; Xulei Qin; Dongsheng Wang; Susan Muller; Hongzheng Zhang; Amy Chen; Zhuo Georgia Chen; Baowei Fei
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Paper Abstract

We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin stained tissue from tongue sections were captured for classifying tumor and non-tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer.

Paper Details

Date Published: 23 March 2016
PDF: 10 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910L (23 March 2016); doi: 10.1117/12.2217286
Show Author Affiliations
Guolan Lu, Georgia Institute of Technology (United States)
Emory Univ. (United States)
Xulei Qin, Emory Univ. (United States)
Dongsheng Wang, Emory Univ. (United States)
Susan Muller, Emory Univ. School of Medicine (United States)
Hongzheng Zhang, Emory Univ. School of Medicine (United States)
Amy Chen, Emory Univ. School of Medicine (United States)
Zhuo Georgia Chen, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)


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

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