Mammogram Breast Density Measurement via Pixel Probability
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Solution Overview
Problem
Current methods for detecting breast cancer through mammograms are subjective and prone to error, as they rely on radiologists to identify abnormalities, and existing automated systems struggle to accurately assess changes in breast density, which is a critical risk factor for breast cancer.
Innovation Solution
A method using a statistical learning scheme to derive a parameter that reflects changes in breast density by computing a quotient value representative of tissue structure aspect ratios and classifying pixels using a trained classifier, enabling more accurate and sensitive measurements of breast density changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiologists manually examine mammograms to identify abnormalities, then human expertise and judgment are utilized, but subjectivity and error are introduced into the detection process
Solution Approach 1:
The patent replaces the manual mechanical examination process with an automated computer-based system that uses image processing algorithms to objectively measure breast density and detect abnormalities, eliminating human subjectivity while maintaining detection capability
Solution Approach 2:
The patent transforms the subjective visual assessment into objective quantitative parameters by measuring breast density as a continuous variable and using standardized metrics (such as percentage of dense tissue) to characterize mammographic findings, enabling precise and reproducible measurements
2Measurement precision
If automated systems are used to assess breast density, then objectivity is improved, but the ability to accurately detect subtle density changes is reduced
Solution Approach 1:
The patent applies partial action by focusing the automated system specifically on measuring breast density parameters rather than attempting to replicate all aspects of radiologist interpretation, allowing the system to excel at its specialized measurement function while radiologists retain overall diagnostic responsibility
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated density measurements are integrated into the overall diagnostic workflow, allowing radiologists to review and interpret the quantitative data in context with other clinical information, thereby combining automated precision with human judgment
3Adaptability or versatility
If subjective radiologist assessment is used, then clinical judgment is applied, but reproducibility and consistency across different examinations are poor
Solution Approach 1:
The patent transforms subjective categorical assessments into objective continuous parameters, measuring breast density as a quantifiable value (e.g., percentage of dense tissue area) that can be precisely measured and reproduced across different examinations and radiologists
Solution Approach 2:
The patent creates a universal measurement system that can be applied consistently across all mammographic examinations regardless of which radiologist performs the initial reading, establishing a standardized method for assessing breast density that works across different practitioners and facilities
Data Source
AI summary
A method of processing a mammogram image to derive a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being an aggregate probability score reflecting the probability of the image being a member of a predefined class of mammogram images, comprises computing for each of a multitude of pixels within a large region of interest within the image a pixel probability score assigned by a trained statistical classifier according to the probability of said pixel belonging to an image belonging to said class, said pixel probability being calculated on the basis of a selected plurality of features of said pixels, and computing said parameter by aggregating the pixel probability scores over said region of interest. Said features may include the 3-jet of said pixels.