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 language | English (US) |
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Article number | 106004 |
Journal | Journal of biomedical optics |
Volume | 19 |
Issue number | 10 |
DOIs | |
State | Published - Oct 1 2014 |
Externally published | Yes |
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