@inproceedings{93d84b2229a04c64a317f9d9469398d4,
title = "Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging",
abstract = "Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two di- mensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and ob-tains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.",
keywords = "Feature extraction, Head and neck cancer, Hyperspectral imaging, Image classification, Principal component analysis (PCA), Superpixels, Support vector machine (SVM)",
author = "Hyunkoo Chung and Guolan Lu and Zhiqiang Tian and Dongsheng Wang 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 = "10.1117/12.2216559",
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",
}