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

Guolan Lu, Dongsheng Wang, Xulei Qin, Luma Halig, Susan Muller, Hongzheng Zhang, Amy Chen, Brian W. Pogue, Zhuo Georgia Chen, Baowei Fei

Research output: Contribution to journalArticle

24 Citations (Scopus)

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.

Original languageEnglish (US)
Article number126012
JournalJournal of biomedical optics
Volume20
Issue number12
DOIs
StatePublished - Dec 1 2015
Externally publishedYes

Fingerprint

surgery
Surgery
image processing
animals
Tumors
Animals
Image processing
tumors
Image classification
cancer
image classification
Glare
glare
Image analysis
Feature extraction
preprocessing
image analysis
pattern recognition
Tissue
Derivatives

Keywords

  • cancer surgery
  • feature extraction
  • feature selection
  • glare removal
  • hyperspectral imaging
  • image registration
  • intra-operative cancer detection
  • mutual information

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering

Cite this

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery. / Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W.; Chen, Zhuo Georgia; Fei, Baowei.

In: Journal of biomedical optics, Vol. 20, No. 12, 126012, 01.12.2015.

Research output: Contribution to journalArticle

Lu, Guolan ; Wang, Dongsheng ; Qin, Xulei ; Halig, Luma ; Muller, Susan ; Zhang, Hongzheng ; Chen, Amy ; Pogue, Brian W. ; Chen, Zhuo Georgia ; Fei, Baowei. / Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery. In: Journal of biomedical optics. 2015 ; Vol. 20, No. 12.
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