29 March 2016 Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging
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Abstract
Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional 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 obtains 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.
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Hyunkoo Chung, Hyunkoo Chung, Guolan Lu, Guolan Lu, Zhiqiang Tian, Zhiqiang Tian, Dongsheng Wang, Dongsheng Wang, Zhuo Georgia Chen, Zhuo Georgia Chen, Baowei Fei, Baowei Fei, } "Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978813 (29 March 2016); doi: 10.1117/12.2216559; https://doi.org/10.1117/12.2216559
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