Research Papers: Imaging

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

[+] Author Affiliations
Guolan Lu

Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30332, United States

Dongsheng Wang, Zhuo Georgia Chen

Emory University, School of Medicine, Department of Hematology and Medical Oncology, , Atlanta, Georgia 30332, United States

Xulei Qin, Luma Halig

Emory University, School of Medicine, Department of Radiology and Imaging Sciences, , Atlanta, Georgia 30332, United States

Susan Muller, Hongzheng Zhang, Amy Chen

Emory University, School of Medicine, Department of Otolaryngology, , Atlanta, Georgia 30332, United States

Brian W. Pogue

Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire 03755, United States

Baowei Fei

Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30332, United States

Emory University, School of Medicine, Department of Radiology and Imaging Sciences, , Atlanta, Georgia 30332, United States

Winship Cancer Institute of Emory University, Atlanta, Georgia 30322, United States

J. Biomed. Opt. 20(12), 126012 (Dec 28, 2015). doi:10.1117/1.JBO.20.12.126012
History: Received July 4, 2015; Accepted November 25, 2015
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Abstract.  Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Guolan Lu ; Dongsheng Wang ; Xulei Qin ; Luma Halig ; Susan Muller, et al.
"Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery", J. Biomed. Opt. 20(12), 126012 (Dec 28, 2015). ; http://dx.doi.org/10.1117/1.JBO.20.12.126012


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