ArticleProceedings of SPIE--the International Society for Optical Engineering2014
A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- An Ensemble Learning Method for Detection of Head and Neck Squamous Cell Carcinoma Using Polarized Hyperspectral Microscopic Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and machine learning.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Development of a new polarized hyperspectral imaging microscope.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Article
- Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis.Journal of biophotonics · 2018Article
- Tumor Margin Classification of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal CarcinomaFrontiers in oncology · 2018Article
- A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging.IEEE transactions on bio-medical engineering · 2016Article
- Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
- A minimum spanning forest based classification method for dedicated breast CT images.Medical physics · 2015Article
- Estimation of Tissue Optical Parameters with Hyperspectral Imaging and Spectral Unmixing.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery.Journal of biomedical optics · 2015Article
- Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging.Journal of biomedical optics · 2014Article
- Polarized Hyperspectral Microscopic Imaging for White Blood Cells on Wright-Stained Blood Smear Slides.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Polarized Hyperspectral Microscopic Imaging for Zebrafish.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Polarized hyperspectral microscopic imaging for collagen visualization on pathologic slides of head and neck squamous cell carcinoma.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.