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Related Experiment Video

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Thermography based breast cancer detection using texture features and minimum variance quantization.

Marina Milosevic¹, Dragan Jankovic², Aleksandar Peulic³

¹Department of Computer Engineering, Faculty of Technical Sciences, University of Kragujevac, Serbia.

|September 30, 2015

Summary

This summary is machine-generated.

This study introduces an AI system for breast thermogram analysis, accurately detecting abnormal patterns using texture features and image segmentation. The K-Nearest Neighbor classifier achieved 92.5% accuracy in identifying malignant tissues.

Keywords:

breast cancer breast classification breast segmentation texture analysis thermography

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Last Updated: Apr 1, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Published on: May 5, 2011

Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

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Published on: April 5, 2020

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Breast thermography offers a non-invasive method for detecting thermal anomalies.
Accurate analysis of thermographic images is crucial for early breast cancer diagnosis.
Automated systems can enhance the efficiency and reliability of thermogram interpretation.

Purpose of the Study:

To develop and evaluate a system for detecting and diagnosing abnormal patterns in breast thermograms.
To assess the effectiveness of texture features derived from Gray-Level Co-occurrence Matrices (GLCM) for classification.
To investigate the utility of image segmentation techniques for localizing suspicious regions.

Main Methods:

Feature extraction using 20 GLCM features to quantify textural information.
Classification of thermograms into normal and abnormal patterns using Support Vector Machine, Naive Bayes, and K-Nearest Neighbor classifiers.
Image segmentation techniques (minimum variance quantization, dilation, erosion) applied to isolate regions of interest.

Main Results:

The K-Nearest Neighbor classifier achieved the highest classification accuracy of 92.5%.
Five-fold cross-validation and Receiver Operating Characteristic (ROC) analysis confirmed classification performance.
Image segmentation techniques demonstrated potential in accurately extracting the shape of suspected tumors.

Conclusions:

The proposed system effectively detects abnormal patterns in breast thermograms.
GLCM features combined with K-Nearest Neighbor classification provide a robust diagnostic approach.
Image segmentation aids in precise localization and characterization of potential abnormalities.