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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

CMAWRNet: Multiple Adverse Weather Removal via a Unified Quaternion Neural Architecture.

Journal of imaging·2025
Same author

Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.

Bioengineering (Basel, Switzerland)·2025
Same author

QRNet: A Quaternion-Based Retinex Framework for Enhanced Wireless Capsule Endoscopy Image Quality.

Bioengineering (Basel, Switzerland)·2025
Same author

Novel Entropy for Enhanced Thermal Imaging and Uncertainty Quantification.

Entropy (Basel, Switzerland)·2024
Same author

Detection and analysis of deteriorated areas in solar PV modules using unsupervised sensing algorithms and 3D augmented reality.

Heliyon·2024
Same author

LRENet: a location-related enhancement network for liver lesions in CT images.

Physics in medicine and biology·2024

Related Experiment Video

Updated: Sep 18, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K

Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches.

Larry Ryan1, Sos Agaian1

  • 1Department of Computer Science, Graduate Center, CUNY, City University of New York, New York, NY 10016, USA.

Bioengineering (Basel, Switzerland)
|June 26, 2025
PubMed
Summary

Infrared thermography combined with texture analysis and machine learning offers a promising, low-cost method for early breast cancer detection. This approach addresses limitations of traditional screening, aiming to improve accuracy and reduce radiologist workload.

Keywords:
breast cancerimage processingmedical image analysistexturethermography

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K
Infrared Thermography for the Detection of Changes in Brown Adipose Tissue Activity
08:16

Infrared Thermography for the Detection of Changes in Brown Adipose Tissue Activity

Published on: September 28, 2022

2.3K

Related Experiment Videos

Last Updated: Sep 18, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K
Infrared Thermography for the Detection of Changes in Brown Adipose Tissue Activity
08:16

Infrared Thermography for the Detection of Changes in Brown Adipose Tissue Activity

Published on: September 28, 2022

2.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of death, necessitating improved early detection methods.
  • Mammography faces cost and accessibility challenges, particularly in resource-limited settings.
  • Infrared thermography (IRT) presents a non-invasive, radiation-free alternative for detecting thermal signatures of tumors.

Purpose of the Study:

  • To survey the integration of texture analysis and machine learning with IRT for breast cancer detection.
  • To address gaps in current literature regarding advanced IRT analysis techniques.
  • To identify limitations and future research directions in this field.

Main Methods:

  • Comprehensive review of the IRT processing pipeline: image preprocessing, feature extraction, and classification.
  • Focus on texture analysis and machine learning algorithms applied to thermal images.
  • Critical analysis of existing datasets and performance assessment methodologies.

Main Results:

  • The integration of texture analysis and machine learning in IRT shows high potential for breast cancer detection.
  • Current methods face limitations, including reliance on small datasets and lack of widespread clinical validation.
  • This approach has the potential to significantly enhance diagnostic accuracy and reduce the burden on radiologists.

Conclusions:

  • Texture analysis and machine learning significantly enhance the capabilities of infrared thermography for breast cancer screening.
  • Addressing current limitations, such as dataset size and standardization, is crucial for clinical adoption.
  • Further research is needed to optimize algorithms and validate findings for widespread use in early breast cancer detection.