Serial Skin Image Registration for Accurate Lesion Change Assessment
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Solution Overview
Problem
Conventional skin cancer screening methods are time-consuming and prone to misdiagnosis due to human limitations in detecting subtle changes in lesions over time, exacerbated by variations in image quality and evaluator expertise.
Innovation Solution
A method and system for registering multiple patient images over time, involving image pre-processing, coarse and high-resolution alignment, segmentation, and analysis to accurately assess changes in lesions, accommodating variations in posture, lighting, and image capture systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional physical examination and static photograph comparison are used for skin cancer screening, then the process is simple to perform, but it is time-consuming and prone to misdiagnosis due to human limitations in detecting subtle changes
Solution Approach 1:
The patent replaces the mechanical human visual examination system with an automated image processing system that uses computer algorithms to detect and measure lesion changes. The system automatically compares serial images, calculates lesion metrics (area, perimeter, diameter), and identifies changes without human intervention, thereby eliminating time loss while improving measurement precision.
Solution Approach 2:
The patent creates digital copies of patient skin images and uses these copies for automated analysis. By working with digital image copies rather than physical photographs, the system enables precise computational measurement and comparison across multiple time points, significantly improving detection accuracy while reducing the time required for manual review.
2Measurement precision
If manual comparison of serial photographs is performed, then the method is easy to implement, but it provides limited utility due to human perception limitations under suboptimal conditions
Solution Approach 1:
The patent segments the complex image analysis task into distinct computational steps: image pre-processing, feature detection (lesion identification), metric calculation (area, perimeter, diameter), and change detection. This segmentation allows each step to be optimized independently while maintaining overall system manageability, improving measurement precision without overwhelming complexity.
Solution Approach 2:
The patent transforms qualitative visual assessment into quantitative parameter measurements. By calculating specific parameters (lesion area in pixels, perimeter length, diameter measurements) and comparing these numerical values across serial images, the system achieves high measurement precision while keeping the processing system relatively simple through standardized computational formulas.
3Reliability
If automated image analysis is implemented, then objective and reproducible assessment is achieved, but the system complexity increases significantly
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously refines its measurements by comparing new images against established baseline data. The automated analysis provides consistent, repeatable measurements that feed back into the patient's medical record, enabling longitudinal tracking with high reliability. The system's deterministic algorithms ensure the same input images always produce the same output measurements, guaranteeing reproducibility.
Solution Approach 2:
The patent enables the image processing system to perform self-validation through automated quality control checks. The system independently verifies image alignment, detects measurement consistency, and flags anomalies without requiring external manual verification. This self-service capability maintains high reliability while minimizing the need for complex external validation systems.
Data Source
AI summary
A system, method, and computer program product for registering two or more patient images for change assessment over time. An example aspect is configured to: obtain a new image of an area with an image capture system; obtain a reference image of a similar area; perform pre-processing of the new image and the reference image; perform a coarse alignment of the new image and the reference image; perform a high-resolution estimate; perform a high-resolution alignment; cross-check the at least one-point match to eliminate false matches and confirm correct matches of the high-resolution new image and the high-resolution reference image; perform segmentation of the high-resolution new image and the high-resolution reference image; perform analysis on at least one lesion in the high-resolution new image and the high-resolution reference image; and display a result of the analysis on a validator.