title: Spectral-Spatial Classification Using Tensor Modeling for Cancer Detection with Hyperspectral Imaging.
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.
authors.affiliation: The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA.
authors.forename: Guolan
authors.initials: G
authors.lastname: Lu
authors.affiliation: Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
authors.forename: Luma
authors.initials: L
authors.lastname: Halig
authors.affiliation: Department of Hematology and Medical Oncology, Emory University, Atlanta, GA.
authors.forename: Dongsheng
authors.initials: D
authors.lastname: Wang
authors.affiliation: Department of Hematology and Medical Oncology, Emory University, Atlanta, GA.
authors.forename: Zhuo Georgia
authors.initials: ZG
authors.lastname: Chen
authors.affiliation: The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA ; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA ; Department of Mathematics & Computer Science, Emory University, Atlanta, GA ; Winship Cancer Institute of Emory University, Atlanta, GA.
authors.forename: Baowei
authors.initials: B
authors.lastname: Fei
keywords: Dimension reduction; Feature ranking; Head and neck cancer; Hyperspectral imaging; Tensor modeling; Tucker tensor decomposition