Thermal Imaging Techniques for Breast Screening - A Survey

Area of Science:

• Biomedical Imaging
• Medical Diagnostics
• Computational Imaging

Background:

• Breast cancer is a leading cause of death in women, with cure rates dropping significantly with later-stage diagnosis.
• Current screening methods are costly, complex, and involve radiation exposure.
• Medical thermography presents a non-invasive, radiation-free alternative for breast cancer screening, particularly effective in dense breast tissues.

Purpose of the Study:

• To review and elaborate on image processing methods for enhanced breast cancer detection using medical thermography.
• To address the challenges of low contrast and signal-to-noise ratio in thermal images for accurate diagnosis.
• To explore the application of machine learning algorithms for identifying abnormal thermograms through asymmetry analysis.

Main Methods:

• Image segmentation to isolate breast regions from background.
• Separation of segmented images into symmetrical left and right breast tissues.
• Extraction of statistical and histogram features for machine learning-based abnormality detection.
• Enhancement of image contrast and signal-to-noise ratio for clearer tissue boundary identification.

Main Results:

• Image processing techniques can overcome the inherent limitations of thermal images (low contrast, low SNR, amorphous nature).
• Asymmetry analysis of processed thermal images aids in identifying potential pathological conditions.
• Machine learning algorithms effectively utilize extracted features to distinguish between normal and abnormal breast thermograms.

Conclusions:

• Advanced image processing is crucial for improving the diagnostic significance of medical thermography in breast cancer detection.
• Thermography, when coupled with sophisticated image analysis, offers a promising non-invasive screening tool.
• Further development in these techniques can lead to earlier and more accurate breast cancer diagnosis.