In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction

Ling Ma; Martin Halicek; Baowei Fei

doi:10.1117/12.2549397

28 February 2020 In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction

Ling Ma, Martin Halicek, Baowei Fei

Author Affiliations +

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113171C (2020) https://doi.org/10.1117/12.2549397
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Ling Ma, Martin Halicek, and Baowei Fei "In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171C (28 February 2020); https://doi.org/10.1117/12.2549397

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