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Proceedings Paper

In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction
Author(s): Ling Ma; Martin Halicek; Baowei Fei
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Paper Abstract

Hyperspectral imaging (HSI) is a promising optical imaging technique for cancer detection. However, quantitative methods need to be developed in order to utilize the rich spectral information and subtle spectral variation in such images. In this study, we explore the feasibility of using wavelet-based features from in vivo hyperspectral images for head and neck cancer detection. Hyperspectral reflectance data were collected from 12 mice bearing head and neck cancer. Catenation of 5-level wavelet decomposition outputs of hyperspectral images was used as a feature for tumor discrimination. A support vector machine (SVM) was utilized as the classifier. Seven types of mother wavelets were tested to select the one with the best performance. Classifications with raw reflectance spectra, 1-level wavelet decomposition output, and 2-level wavelet decomposition output, as well as the proposed feature were carried out for comparison. Our results show that the proposed wavelet-based feature yields better classification accuracy, and that using different type and order of mother wavelet achieves different classification results. The wavelet-based classification method provides a new approach for HSI detection of head and neck cancer in the animal model.

Paper Details

Date Published: 28 February 2020
PDF: 9 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171C (28 February 2020); doi: 10.1117/12.2549397
Show Author Affiliations
Ling Ma, The Univ. of Texas at Dallas (United States)
Tianjin Univ. (China)
Martin Halicek, The Univ. of Texas at Dallas (United States)
Georgia Institute of Technology & Emory Univ. School of Medicine (United States)
Medical College of Georgia, Augusta Univ. (United States)
Baowei Fei, The Univ. of Texas at Dallas (United States)
The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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