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Proceedings Paper

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging
Author(s): Hyunkoo Chung; Guolan Lu; Zhiqiang Tian; Dongsheng Wang; Zhuo Georgia Chen; Baowei Fei
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Paper 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.

Paper Details

Date Published: 29 March 2016
PDF: 8 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978813 (29 March 2016); doi: 10.1117/12.2216559
Show Author Affiliations
Hyunkoo Chung, Georgia Institute of Technology (United States)
Emory Univ. (United States)
Guolan Lu, Georgia Institute of Technology (United States)
Emory Univ. (United States)
Zhiqiang Tian, Emory Univ. (United States)
Dongsheng Wang, Emory Univ. (United States)
Zhuo Georgia Chen, Emory Univ. (United States)
Baowei Fei, Georgia Institute of Technology (United States)
Winship Cancer Institute, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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