Optical Flow Skin Anomaly Detection via Nonlinear Registration
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Solution Overview
Problem
Current skin cancer screening methods, including automated imaging systems, face challenges with misalignment of images due to nonlinear changes in skin composition, leading to high rates of false-positives and false-negatives, which hinder accurate detection of skin anomalies.
Innovation Solution
The implementation of an optical flow system that generates flow vectors between images and uses an analyzer to identify anomalies, refining image registration through coarse and fine registration modules, including affine and optical flow registration processes, to accurately detect changes in skin features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional affine image registration algorithms are used to detect anomalies in skin composition, then the system achieves automated detection capability, but the linear alignment approach causes misalignment due to nonlinear changes in skin composition (such as weight gain or loss), leading to high rates of false-positives and false-negatives
Solution Approach 1:
The patent transforms the image registration problem from a linear affine transformation to a nonlinear parameter-based transformation. By representing skin surface features through parametric models that can adapt to nonlinear deformations (such as changes in body weight), the system maintains accurate alignment while preserving automated detection capability. The parameters are adjusted to account for nonlinear skin composition changes between imaging sessions.
2Device complexity
If linear affine image registration is used to align skin images, then the processing is computationally efficient and simple, but the misalignment of images due to nonlinear skin changes requires manual re-evaluation by physicians, defeating the purpose of automation
Solution Approach 1:
The patent replaces the mechanical linear affine registration process with an intelligent nonlinear registration system that uses parameter-based modeling and pattern recognition. This substitution enables the system to automatically adapt to nonlinear skin deformations without requiring manual intervention, thereby eliminating the time loss associated with physician re-evaluation while maintaining computational feasibility through efficient parameter optimization algorithms.
3Device complexity
If traditional imaging systems rely on linear alignment of skin images, then the system structure remains simple, but the high misdiagnosis rate (false-positives and false-negatives) reduces clinical utility
Solution Approach 1:
The patent enhances measurement precision by changing from fixed linear transformation parameters to adaptive nonlinear parameters that model skin surface variations. The system uses parametric representations of skin features that can dynamically adjust to accommodate nonlinear deformations, thereby significantly reducing false-positives and false-negatives while maintaining a relatively simple overall system structure through the use of standardized parametric models.
Data Source
AI summary
Methods and apparatus for detecting anomalies in images according to various aspects of the present invention operate in conjunction with an optical flow system. The optical flow system may receive image data for a first image and a second image and generate flow vectors corresponding to differences between the first image and the second image. An analyzer may be coupled to the optical flow system and analyze the flow vectors to identify anomalies in the second image.