@inproceedings{cb44a3da86cb4e69a25bf03fb9aba52d,
title = "Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging",
abstract = "As an emerging technology, hyperspectral imaging (HSI) combines both the chemical specificity of spectroscopy and the spatial resolution of imaging, which may provide a non-invasive tool for cancer detection and diagnosis. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. In this paper, we introduce a tensor-based computation and modeling framework for the analysis of hyperspectral images to detect head and neck cancer. The proposed classification method can distinguish between malignant tissue and healthy tissue with an average sensitivity of 96.97% and an average specificity of 91.42% in tumor-bearing mice. The hyperspectral imaging and classification technology has been demonstrated in animal models and can have many potential applications in cancer research and management.",
keywords = "Dimension reduction, Feature ranking, Head and neck cancer, Hyperspectral imaging, Tensor modeling, Tucker tensor decomposition",
author = "Guolan Lu and Luma Halig and Dongsheng Wang and Chen, {Zhuo Georgia} and Baowei Fei",
year = "2014",
doi = "10.1117/12.2043796",
language = "English (US)",
isbn = "9780819498274",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2014",
note = "Medical Imaging 2014: Image Processing ; Conference date: 16-02-2014 Through 18-02-2014",
}