Automated Mammogram Image Alignment and Registration System
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Solution Overview
Problem
Current methods for mammogram image analysis lack an effective tool for alignment and registration, leading to inconsistencies in identifying and monitoring breast cancer progression over time, as they rely on manual matching and eyeballing, which is burdensome and introduces estimation bias.
Innovation Solution
An image alignment and registration system that includes a processor to convert medical images into binary form, isolate and segment breast regions, remove non-breast tissue, align and register images using bicubic interpolation, and perform survival analysis to predict breast cancer risk, incorporating bivariate splines and Cox proportional hazards models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching and eyeballing methods are used for image alignment, then clinicians can identify regions of interest, but inconsistencies and estimation bias are introduced among clinicians
Solution Approach 1:
The patent replaces the manual mechanical process of eyeballing and hand-matching images with an automated computational system. The system uses coordinate system transformation, image registration algorithms, and computer vision techniques to automatically align mammograms from different time points, eliminating human subjectivity and operational burden while maintaining high precision.
Solution Approach 2:
The image alignment system performs self-service by automatically processing and aligning medical images without requiring clinician intervention for the alignment process itself. The system independently handles coordinate transformation, image registration, and region matching, freeing clinicians to focus on interpretation rather than manual alignment tasks.
2Reliability
If multiple images are aligned and registered on the same coordinate system, then estimation bias and variation are avoided, but no well-accepted tool exists for mammogram registration
Solution Approach 1:
The patent segments the image alignment process into distinct modular components: coordinate system extraction, image preprocessing, feature detection, registration transformation, and validation. This segmentation allows each component to be independently optimized and tested, reducing overall system complexity while ensuring reliable results through systematic processing of each stage.
Solution Approach 2:
The system introduces an intermediary coordinate system that serves as a common reference frame for multiple mammograms. By transforming all images into this standardized coordinate system through mathematical transformations, the system achieves reliable comparison across time points without requiring complex direct pairwise alignment between every image combination.
3Measurement precision
If deep learning approaches are used for risk prediction, then diagnosis facilitation is achieved, but prediction AUC ranges only from 0.70 to 0.72
Solution Approach 1:
The patent merges multiple complementary approaches: traditional image registration and coordinate alignment methods are combined with deep learning-based feature extraction and risk prediction models. This integration allows the system to leverage both the geometric precision of registration techniques and the pattern recognition power of deep learning, achieving superior prediction accuracy beyond what either method achieves alone.
Solution Approach 2:
The prediction system uses a composite methodology that combines multiple data sources and analysis techniques: registered image data, extracted texture features, demographic information, and clinical data are integrated into a unified risk prediction model. This composite approach mirrors the use of composite materials in engineering, where combining different components creates a system with properties superior to individual components alone.
Data Source
AI summary
Among the various aspects of the present disclosure are the provision of an image alignment and registration system and a breast cancer risk prediction system.