@inproceedings{7a5811123a7c4ba19be49f53d1755516,
title = "Quantitative diagnosis of tongue cancer from histological images in an animal model",
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.",
keywords = "4NQO-induced oral cancer, Tongue cancer diagnosis, computer aided diagnosis, histological image classification, random forest, squamous cell carcinoma",
author = "Guolan Lu and Xulei Qin and Dongsheng Wang and Susan Muller and Hongzheng Zhang and Amy Chen and Chen, {Zhuo G.} and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2016 SPIE.; 4th Medical Imaging 2016: Digital Pathology ; Conference date: 02-03-2016 Through 03-03-2016",
year = "2016",
doi = "10.1117/12.2217286",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Anant Madabhushi and Gurcan, {Metin N.}",
booktitle = "Medical Imaging 2016",
}