Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging

Guolan Lu, Luma Halig, Dongsheng Wang, Xulei Qin, Zhuo Georgia Chen, Baowei Fei

Research output: Contribution to journalArticle

57 Scopus citations

Abstract

Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.

Original languageEnglish (US)
Article number106004
JournalJournal of biomedical optics
Volume19
Issue number10
DOIs
StatePublished - Oct 1 2014

Keywords

  • cross validation
  • dimension reduction
  • feature extraction
  • hyperspectral imaging
  • noninvasive cancer detection
  • spectral-spatial classification
  • tensor decomposition

ASJC Scopus subject areas

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

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