Automated Breast Density Measurement via Noise-Field Deconvolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for measuring breast density from mammograms are not automated, making it difficult to use for clinical breast cancer risk assessment due to variations in data representation and lack of standardization across different FFDM technologies.
Innovation Solution
An automated breast density measurement (PDa) is developed using a deconvolution process based on Fourier spectral density modeling, which converts the power-law behavior of mammograms into a noise-field, allowing for the detection of signal-dependent noise, and can be applied to both raw and processed images from specific FFDM technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated measurement methods are implemented, then measurement precision and reproducibility are improved, but the complexity of the measurement system increases
Solution Approach 1:
The patent replaces manual operator-assisted measurement methods with an automated computational algorithm that processes mammogram images. The system uses digital image processing techniques, including thresholding methods and region-of-interest analysis, to automatically quantify breast density percentages, eliminating the need for manual measurement while improving precision and reproducibility.
Solution Approach 2:
The patent transforms the measurement approach by changing from manual visual assessment to automated digital parameter extraction. The system converts mammogram images into quantitative data through computational algorithms, measuring density percentages and other parameters automatically, thereby reducing operator variability and improving measurement consistency.
2Ease of operation
If operator-assisted measurement methods are used, then ease of operation is maintained, but measurement precision and reproducibility deteriorate due to operator variability
Solution Approach 1:
The measurement system performs self-service by automatically processing mammogram images without requiring operator intervention for each measurement. The automated algorithm independently analyzes images, applies thresholding criteria, and calculates density percentages consistently, eliminating operator variability while maintaining ease of use through automated operation.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated measurement results can be reviewed and adjusted if necessary. The algorithm provides consistent quantitative feedback that can be compared across different measurements and operators, ensuring reproducibility while allowing for quality control and verification of results.
3Measurement precision
If standardized measurement protocols are implemented across different FFDM technologies, then measurement precision is improved, but adaptability to different technologies deteriorates
Solution Approach 1:
The patent develops a universal measurement protocol that can be applied across different FFDM technologies. The automated algorithm is designed to process images from various manufacturers and systems using common image processing techniques, enabling consistent breast density measurement across different technological platforms without requiring technology-specific customization.
Solution Approach 2:
The system adapts to different FFDM technologies by adjusting measurement parameters and thresholds based on the specific characteristics of each system. The algorithm can modify its processing parameters to accommodate different image qualities, contrast characteristics, and resolution levels while maintaining standardized measurement protocols and precision across technologies.
Data Source
AI summary
An automated percentage of breast density measure (PDa) that analyzes signal dependent noise (SDN) based on a wavelet expansion using full field digital mammography (FFDM). A matched case-control dataset is used with images acquired from a specific direct x-ray capture FFDM system. PDa is applied to the raw and clinical display representation images. Transforming (pixel mapping) of the raw image to another representation (raw-transformed) is performed using differential evolution optimization and investigated the influence of lowering the native spatial resolution of the image by a one-half. When controlling for body mass index, the quartile specific ORs for the associations of PDa with breast cancer varied with representation and resolution. PDa is a valid automated breast density measurement for a specific FFDM technology and compares well against PD (operator-assisted or the standard) when applied to either the raw-transformed or clinical display images from this FFDM technology.