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

25 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationImage Processing
PublisherSPIE
ISBN (Print)9780819498274
DOIs
StatePublished - Jan 1 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
Volume9034
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2014: Image Processing
CountryUnited States
CitySan Diego, CA
Period2/16/142/18/14

Fingerprint

Tensors
Bearings (structural)
cancer
tensors
Tissue
Tumors
Neoplasms
Animals
Spectroscopy
Technology
Imaging techniques
animal models
Head and Neck Neoplasms
lesions
mice
emerging
Spectrum Analysis
tumors
Animal Models
spatial resolution

Keywords

  • 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

Cite this

Lu, G., Halig, L., Wang, D., Chen, Z. G., & Fei, B. (2014). Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging. In Medical Imaging 2014: Image Processing [903413] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9034). SPIE. https://doi.org/10.1117/12.2043796

Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging. / Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei.

Medical Imaging 2014: Image Processing. SPIE, 2014. 903413 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9034).

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

Lu, G, Halig, L, Wang, D, Chen, ZG & Fei, B 2014, Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging. in Medical Imaging 2014: Image Processing., 903413, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9034, SPIE, Medical Imaging 2014: Image Processing, San Diego, CA, United States, 2/16/14. https://doi.org/10.1117/12.2043796
Lu G, Halig L, Wang D, Chen ZG, Fei B. Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging. In Medical Imaging 2014: Image Processing. SPIE. 2014. 903413. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2043796
Lu, Guolan ; Halig, Luma ; Wang, Dongsheng ; Chen, Zhuo Georgia ; Fei, Baowei. / Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging. Medical Imaging 2014: Image Processing. SPIE, 2014. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{cb44a3da86cb4e69a25bf03fb9aba52d,
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.",
keywords = "Dimension reduction, Feature ranking, Head and neck cancer, Hyperspectral imaging, Tensor modeling, Tucker tensor decomposition",
author = "Guolan Lu and Luma Halig and Dongsheng Wang and Chen, {Zhuo Georgia} and Baowei Fei",
year = "2014",
month = "1",
day = "1",
doi = "10.1117/12.2043796",
language = "English (US)",
isbn = "9780819498274",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2014",

}

TY - GEN

T1 - Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging

AU - Lu, Guolan

AU - Halig, Luma

AU - Wang, Dongsheng

AU - Chen, Zhuo Georgia

AU - Fei, Baowei

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

KW - Dimension reduction

KW - Feature ranking

KW - Head and neck cancer

KW - Hyperspectral imaging

KW - Tensor modeling

KW - Tucker tensor decomposition

UR - http://www.scopus.com/inward/record.url?scp=84902108637&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902108637&partnerID=8YFLogxK

U2 - 10.1117/12.2043796

DO - 10.1117/12.2043796

M3 - Conference contribution

C2 - 25328639

AN - SCOPUS:84902108637

SN - 9780819498274

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2014

PB - SPIE

ER -