@inproceedings{acd97a4b682f4feb9566542131aa43ba,
title = "Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis",
abstract = "Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.",
keywords = "4NQO-induced oral cancer, Early cancer detection, Hyperspectral imaging, Image classification, Random forest, Superpixel",
author = "Guolan Lu and Xulei Qin and Dongsheng Wang and Susan Muller and Hongzheng Zhang and Amy Chen and Chen, {Zhuo Georgia} and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2016 SPIE.; Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 01-03-2016 Through 03-03-2016",
year = "2016",
doi = "10.1117/12.2216553",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Barjor Gimi and Andrzej Krol",
booktitle = "Medical Imaging 2016",
}