ArticleIEEE transactions on bio-medical engineering2016
A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging.
Article in IEEE transactions on bio-medical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Hyperspectral imaging in oral oncology: a scoping review.Frontiers in oral health · 2026Pooled it
- An effective feature selection approach based on hybrid Grey Wolf Optimizer and Genetic Algorithm for hyperspectral image.Scientific reports · 2025Article
- Technological Frontiers in Brain Cancer: A Systematic Review and Meta-Analysis of Hyperspectral Imaging in Computer-Aided Diagnosis Systems.Diagnostics (Basel, Switzerland) · 2024Review
- 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
- A multiscale model of the role of microenvironmental factors in cell segregation and heterogeneity in breast cancer development.PLoS computational biology · 2023Article
- Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Identification of DAPI-stained normal, inflammatory, and carcinoma hepatic cells based on hyperspectral microscopy.Biomedical optics express · 2022Article
- Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- 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
- Applications of hyperspectral imaging in the detection and diagnosis of solid tumors.Translational cancer research · 2020Review
- Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2019Article
- In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer.Cancers · 2019Review
- Deep Learning-Based Framework forSensors (Basel, Switzerland) · 2019Article
- Surgical Aid Visualization System for Glioblastoma Tumor Identification based on Deep Learning andProceedings of SPIE--the International Society for Optical Engineering · 2019Article
- Adaptive deep learning for head and neck cancer detection using hyperspectral imaging.Visual computing for industry, biomedicine, and art · 2019Article
- SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples.Sensors (Basel, Switzerland) · 2018Article
- [Hyperspectral imaging of gastrointestinal anastomoses].Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
goalThe purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model.
methodsAn automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information.
conclusionThe MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. SIGNIFICANCE: Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.
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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.