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

• Type of Material: Article
• Authors: Guolan Lu, Georgia Institute of Technology; Dongsheng Wang, Emory University; Xulei Qin, Emory University; Luma Halig, Emory University; Susan Muller, Emory University; Hongzheng Zhang, Emory University; Amy Chen, Emory University; Brian W. Pogue, Dartmouth College; Zhuo Georgia Chen, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2015-12-28
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Final Published Version
• Copyright Statement: © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).
• Final Published Version (URL): http://dx.doi.org/10.1117/1.JBO.20.12.126012
• Title of Journal or Parent Work: Journal of Biomedical Optics
• ISSN: 1083-3668
• Volume: 20
• Issue: 12
• Start Page: 126012
• End Page: 126012
• Grant/Funding Information: This research is supported in part by NIH grants (R01CA156775 and R21CA176684), Georgia Research Alliance Distinguished Scientists Award, Emory SPORE in Head and Neck Cancer (NIH P50CA128613), and Emory Molecular and Translational Imaging Center (NIH P50CA128301).
• Keywords: Optics; Biochemical Research Methods; Science & Technology; Life Sciences & Biomedicine; image registration; Biochemistry & Molecular Biology; SURVIVAL; PATTERN; feature selection mutual information; ACCURACY; cancer surgery; glare removal; MARGINS; hyperspectral imaging; Radiology, Nuclear Medicine & Medical Imaging; Physical Sciences; HEAD; feature extraction; intra-operative cancer detection
• Research Categories: Engineering, Biomedical; Health Sciences, Radiology

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.