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Proceedings Paper

Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging
Author(s): Guolan Lu; Luma Halig; Dongsheng Wang; Zhuo Georgia Chen; Baowei Fei
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Paper 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.

Paper Details

Date Published: 21 March 2014
PDF: 11 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903413 (21 March 2014); doi: 10.1117/12.2043796
Show Author Affiliations
Guolan Lu, Georgia Institute of Technology (United States)
Emory Univ. (United States)
Luma Halig, 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)
Emory Univ. (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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