Data-Type Assessment for Real-Time Hyperspectral Classification in Medical Imaging-Reference-Cited by-同舟云学术

Data-Type Assessment for Real-Time Hyperspectral Classification in Medical Imaging

Published:2022 Issue: Volume: Page:123-135
ISSN:0302-9743
Container-title:Design and Architecture for Signal and Image Processing
language:
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Author:

Villa Manuel^ORCID,Sancho Jaime^ORCID,Vazquez Guillermo^ORCID,Rosa Gonzalo^ORCID,Urbanos Gemma^ORCID,Martin-Perez Alberto^ORCID,Sutradhar Pallab^ORCID,Salvador Rubén^ORCID,Chavarrías Miguel^ORCID,Lagares Alfonso^ORCID,Juarez Eduardo^ORCID,Sanz César^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-12748-9_10

Reference9 articles.

1. Fabelo, H., et al.: An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation. Sensors 18, 430 (2018). https://doi.org/10.3390/s18020430

2. Lu, G., Fei, B.: Medical hyperspectral imaging: a review. J. Biomed. Opt. 19(1) 010901 (2014). https://doi.org/10.1117/1.JBO.19.1.010901

3. Urbanos, G., et al.: Supervised machine learning methods and hyperspectral imaging techniques jointly applied for brain cancer classification. Sensors 21(11), 3827 (2021). https://doi.org/10.3390/s21113827

4. Sancho, J., et al.: An embedded GPU accelerated hyperspectral video classification system in real-time. In: XXXVI Conference on Design of Circuits and Integrated Systems, Vila do Conde, Portugal (2021). https://doi.org/10.1109/DCIS53048.2021.9666171

5. Al-Sarayreh, M., et. al.: Deep spectral-spatial features of snapshot HS images for red-meat classification. In: 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1–6 (2018). https://doi.org/10.1109/IVCNZ.2018.8634783

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