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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Decoding Breast Cancer in X-ray Mammograms: A Multi-Parameter Approach Using Fractals, Multifractals, and Structural
Santanu Maity1, Mousa Alrubayan1, Prabhakar Pradhan1
1Department of Physics and Astronomy, Mississippi State University, Mississippi State, MS, USA, 39762.
Biorxiv : the Preprint Server for Biology
|June 12, 2025
Summary
Researchers developed new fractal analysis methods to detect breast cancer progression using mammogram images. These quantitative biomarkers showed promise in differentiating benign from cancerous tissues, improving diagnostic accuracy.
Area of Science:
- Biomedical Imaging
- Quantitative Pathology
- Medical Physics
Background:
- Breast cancer diagnosis relies on imaging, but quantitative biomarkers for progression are needed.
- Fractal analysis offers a way to quantify complex structures in medical images.
Purpose of the Study:
- To explore fractal and multifractal characteristics of breast mammogram micrographs for quantitative breast cancer biomarkers.
- To evaluate a novel fractal-functional distribution method and intensity threshold variations.
Main Methods:
- Conventional fractal and multifractal analyses were performed.
- A fractal-functional distribution method was employed for robust statistical interpretation.
- Intensity thresholds and Inverse Participation Ratio (IPR) light localization were analyzed.
Main Results:
- Fractal, multifractal, and fractal-functional parameters differentiated benign from cancerous tissue.
- Intensity-based fractal measures showed distinct patterns in cancer cases based on threshold variations.
- IPR quantified microscopic structural disorder, complementing spatial complexity metrics.
Conclusions:
- The multi-parametric approach integrating spatial complexity and structural disorder shows potential for enhanced breast cancer detection.
- Novel quantitative biomarkers derived from fractal analysis can aid in assessing breast cancer progression.
- This framework may improve the sensitivity and specificity of breast cancer diagnostics.
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