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

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

doi:10.1117/12.2043796

21 March 2014 Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging

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

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 903413 (2014) https://doi.org/10.1117/12.2043796
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

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.

Citation Download Citation

Guolan Lu, Luma Halig, Dongsheng Wang, Zhuo Georgia Chen, and Baowei Fei "Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903413 (21 March 2014); https://doi.org/10.1117/12.2043796

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