Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

Hyunkoo Chung; Guolan Lu; Zhiqiang Tian; Dongsheng Wang; Zhuo Georgia Chen; Baowei Fei

doi:10.1117/12.2216559

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

Hyunkoo Chung
, Guolan Lu
, Zhiqiang Tian
, Dongsheng Wang
, Zhuo Georgia Chen
, Baowei Fei

Georgia Institute of Technology
Emory University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

29 Scopus citations

Abstract

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two di- mensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and ob-tains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

Original language	English
Title of host publication	Medical Imaging 2016
Subtitle of host publication	Biomedical Applications in Molecular, Structural, and Functional Imaging
Editors	Barjor Gimi, Andrzej Krol
Publisher	SPIE
ISBN (Electronic)	9781510600232
DOIs	https://doi.org/10.1117/12.2216559
State	Published - 2016
Externally published	Yes
Event	Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, United States Duration: 1 Mar 2016 → 3 Mar 2016

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	9788
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/Territory	United States
City	San Diego
Period	1/03/16 → 3/03/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Feature extraction
Head and neck cancer
Hyperspectral imaging
Image classification
Principal component analysis (PCA)
Superpixels
Support vector machine (SVM)

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10.1117/12.2216559

Cite this

Chung, H., Lu, G., Tian, Z., Wang, D., Chen, Z. G., & Fei, B. (2016). Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging. In B. Gimi, & A. Krol (Eds.), Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging Article 978813 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9788). SPIE. https://doi.org/10.1117/12.2216559

Chung, Hyunkoo ; Lu, Guolan ; Tian, Zhiqiang et al. / Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging. Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. editor / Barjor Gimi ; Andrzej Krol. SPIE, 2016. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).

@inproceedings{b7359144534d41aba77d39d82b8e2d95,

title = "Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging",

abstract = "Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two di- mensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and ob-tains an average sensitivity of 93\% and an average specificity of 85\% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.",

keywords = "Feature extraction, Head and neck cancer, Hyperspectral imaging, Image classification, Principal component analysis (PCA), Superpixels, Support vector machine (SVM)",

author = "Hyunkoo Chung and Guolan Lu and Zhiqiang Tian and Dongsheng Wang and Chen, \{Zhuo Georgia\} and Baowei Fei",

note = "Publisher Copyright: {\textcopyright} 2016 SPIE.; Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 01-03-2016 Through 03-03-2016",

year = "2016",

doi = "10.1117/12.2216559",

language = "英语",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Barjor Gimi and Andrzej Krol",

booktitle = "Medical Imaging 2016",

}

Chung, H, Lu, G, Tian, Z, Wang, D, Chen, ZG & Fei, B 2016, Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging. in B Gimi & A Krol (eds), Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging., 978813, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9788, SPIE, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, San Diego, United States, 1/03/16. https://doi.org/10.1117/12.2216559

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging. / Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang et al.
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. ed. / Barjor Gimi; Andrzej Krol. SPIE, 2016. 978813 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9788).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

AU - Chung, Hyunkoo

AU - Lu, Guolan

AU - Tian, Zhiqiang

AU - Wang, Dongsheng

AU - Chen, Zhuo Georgia

AU - Fei, Baowei

PY - 2016

Y1 - 2016

N2 - Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two di- mensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and ob-tains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

AB - Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two di- mensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and ob-tains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

KW - Feature extraction

KW - Head and neck cancer

KW - Hyperspectral imaging

KW - Image classification

KW - Principal component analysis (PCA)

KW - Superpixels

KW - Support vector machine (SVM)

UR - https://www.scopus.com/pages/publications/84978803763

U2 - 10.1117/12.2216559

DO - 10.1117/12.2216559

M3 - 会议稿件

AN - SCOPUS:84978803763

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

BT - Medical Imaging 2016

A2 - Gimi, Barjor

A2 - Krol, Andrzej

PB - SPIE

T2 - Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging

Y2 - 1 March 2016 through 3 March 2016

ER -

Chung H, Lu G, Tian Z, Wang D, Chen ZG, Fei B. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging. In Gimi B, Krol A, editors, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. SPIE. 2016. 978813. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.2216559

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

Abstract

Publication series

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UN SDGs

Keywords

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