Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Apr 20, 2026

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
11:27

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Published on: September 22, 2013

9.9K

A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection.

Robert Pike1, Samuel K Patton1, Guolan Lu2

  • 1Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.

Proceedings of Spie--The International Society for Optical Engineering
|November 27, 2014
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

High-speed optical tracking and augmented reality platform for image-guided interventions.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

Enhancing 1p/19q Classification in Brain Gliomas Using IDH Status: A Deep Learning Study.

AJNR. American journal of neuroradiology·2026
Same author

Polarized hyperspectral and polarized light microscopic imaging for enhanced visualization of white blood cells.

Journal of biomedical optics·2026
Same author

Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.

Journal of biomedical optics·2026
Same author

Development and validation of a high-resolution hyperspectral imaging system for the retina.

Journal of biomedical optics·2026
Same author

A spatial-spectral vision transformer model for head and neck cancer detection with hyperspectral, RGB, and synthesized RGB histologic images.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Proceedings of SPIE--the International Society for Optical Engineering·2026
See all related articles
This summary is machine-generated.

This study introduces a new hyperspectral imaging method using support vector machines and a minimum spanning forest algorithm for accurate cancer detection. The technique successfully differentiates cancerous from healthy tissue in animal models.

Area of Science:

  • Biomedical optics
  • Medical imaging
  • Computational biology

Background:

  • Hyperspectral imaging (HSI) offers rich spectral information for tissue analysis.
  • Distinguishing cancerous from healthy tissue remains a challenge in medical diagnostics.
  • Developing advanced algorithms is crucial for effective cancer detection using HSI.

Purpose of the Study:

  • To develop and validate a novel classification method for cancer detection using hyperspectral imaging.
  • To integrate support vector machines (SVM) with a minimum spanning forest (MSF) algorithm for enhanced tissue differentiation.
  • To assess the algorithm's performance in classifying cancerous tissue from normal tissue.

Main Methods:

  • Acquisition of spectral information from tissue samples.
Keywords:
Hyperspectral imagingimage classificationminimum spanning forestsupport vector machine

More Related Videos

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.5K

Related Experiment Videos

Last Updated: Apr 20, 2026

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
11:27

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Published on: September 22, 2013

9.9K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.5K
  • Development of a classification algorithm combining SVM and MSF.
  • Application of the algorithm to hyperspectral images of tumor-bearing mice.
  • In vivo imaging experiments for validation.

    • Main Results:

    • The proposed method effectively utilizes spectral information for tissue analysis.
    • Successful differentiation between cancerous and healthy tissue was achieved.
    • In vivo experimental results confirmed the algorithm's applicability for cancer classification.
    • The combined SVM-MSF approach demonstrated robust performance.

    Conclusions:

    • The developed hyperspectral imaging classification method shows significant potential for cancer detection.
    • The integration of SVM and MSF provides a powerful tool for analyzing complex spectral data.
    • This approach is applicable for in vivo cancer tissue classification using hyperspectral images.