Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationImage Processing
ISBN (Print)9780819498274
StatePublished - 2014
Externally publishedYes
EventMedical Imaging 2014: Image Processing - San Diego, CA, United States
Duration: Feb 16 2014Feb 18 2014

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2014: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA


  • Dimension reduction
  • Feature ranking
  • Head and neck cancer
  • Hyperspectral imaging
  • Tensor modeling
  • Tucker tensor decomposition

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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