Fractal Texture Analysis for Breast Cancer Risk Assessment
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Solution Overview
Problem
Current methods for breast cancer risk assessment are costly, information-dependent, and lack objectivity, making it difficult to identify women at high risk for early detection and personalized surveillance plans.
Innovation Solution
A computerized fractal-based texture analysis method is developed to analyze mammographic parenchymal patterns, using fractal dimensions and linear discriminant analysis to extract features from medical images, allowing for the discrimination between high and low-risk groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computerized fractal-based texture analysis is used, then objectivity and accuracy of breast cancer risk assessment are improved, but device complexity increases
Solution Approach 1:
The patent replaces manual clinical assessment methods with automated computerized fractal analysis. The system uses digital image processing algorithms to extract texture features from mammograms, substituting human subjective evaluation with objective computational measurement. This substitution achieves higher precision and consistency while managing complexity through standardized algorithms.
Solution Approach 2:
The patent transforms mammographic images into quantitative fractal dimension parameters through digital processing. By converting visual patterns into mathematical parameters (fractal dimensions, texture descriptors), the system enables objective measurement and comparison. This parameter transformation allows complex visual assessment to be reduced to measurable numerical values, improving accuracy while maintaining system manageability.
2Reliability
If fractal-based texture analysis is applied, then early detection capability is improved, but loss of information increases due to complex image processing requirements
Solution Approach 1:
The patent extracts specific fractal dimension parameters and texture features from complex mammographic images. By isolating and analyzing specific quantitative characteristics (fractal dimension values, texture descriptors) rather than processing the entire image, the system achieves early detection while minimizing information loss. The extraction process identifies and focuses on the most informative features for cancer risk assessment.
Solution Approach 2:
The patent segments the mammographic image analysis into distinct processing stages: image acquisition, fractal dimension calculation, texture feature extraction, and risk assessment. This segmentation allows systematic processing that preserves information at each stage while reducing complexity. By dividing the analysis process into manageable segments, the system maintains information integrity while achieving reliable early detection.
3Ease of operation
If clinical methods for risk assessment are used, then ease of operation is maintained, but productivity decreases due to high cost and information dependency
Solution Approach 1:
The patent implements a system where the computerized analysis performs self-service processing of mammographic images. The automated fractal analysis independently extracts features, calculates risk, and generates assessments without requiring complex manual intervention. This self-service capability maintains operational simplicity while dramatically improving productivity by eliminating time-consuming manual evaluation processes.
Solution Approach 2:
The patent creates a digital copy of the mammographic image for automated analysis, separating the analysis process from the original physical imaging process. This copying allows the system to process images through standardized computational algorithms, improving efficiency and productivity while maintaining ease of operation through automated workflows that replicate and enhance clinical assessment capabilities.
Data Source
AI summary
A computerized method, system and computer program for the computerized fractal-based analysis of a structure as presented in a pattern on a medical image. Image data is generated from the medical image and a region of interest is selected. The image data is digitized and analyzed to reveal fractal-based computer-generated features of a texture of the image data. Then a qualifier is applied to the computer-generated features to obtain fractal characteristics of the image data. A multi-fractal nature is observed for the texture of the region of interest. A marker for assessing a risk of a disease is yielded based on the multi-fractal nature of the texture.