Genetic Algorithm 2D to 3D Image Correlation
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Solution Overview
Problem
Conventional methods for correlating two-dimensional images with three-dimensional representations are resource-intensive, limiting their application in fields like medical imaging and machine vision, and existing techniques for monitoring prosthesis and bone wear are expensive and difficult to implement.
Innovation Solution
A method using a genetic algorithm to iteratively determine the best fit between a two-dimensional image and a three-dimensional representation by generating fitness functions, breeding new position and orientation values, and repeating the process until an acceptable solution is converged, which can be implemented in systems with reduced processing capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional automated matching approaches are used to correlate two-dimensional images with three-dimensional representations, then matching accuracy is improved, but processing capacity requirements increase significantly
Solution Approach 1:
The patent segments the complex matching problem into distinct phases: generating candidate positions using genetic algorithms, evaluating fitness of each candidate, and iteratively improving solutions. This segmentation allows the system to handle the computational complexity in manageable steps rather than requiring overwhelming processing power for a single monolithic operation.
Solution Approach 2:
The patent employs dynamic iterative optimization where the solution evolves over multiple generations through genetic algorithms. Rather than using a static, resource-intensive conventional matching approach, the system dynamically adapts and refines candidate solutions across iterations, converging toward accurate matches while maintaining reasonable processing requirements.
2Measurement precision
If reference beads are inserted in prostheses and bones to monitor wear and migration, then measurement precision is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The patent extracts the need for physical reference beads by using natural anatomical landmarks and features visible in standard imaging. Instead of adding foreign reference objects to the system, the method utilizes existing structural features of the prosthesis and bone, thereby eliminating the complexity of bead insertion while maintaining measurement capability.
Solution Approach 2:
The patent creates a virtual three-dimensional model that replicates the prosthesis and surrounding anatomy, allowing wear and migration to be monitored by comparing the virtual model against actual imaging data. This virtual copying eliminates the need for physical reference markers while preserving the ability to accurately track positional changes.
3Measurement precision
If image acquisition repeatability is improved to enable accurate wear calculation from two-dimensional images, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The patent transitions from relying on repeatability of two-dimensional image acquisition to using three-dimensional virtual representations. By adding the dimensional aspect and using genetic algorithms to match 2D images with 3D models, the system eliminates the need for strict image acquisition repeatability while maintaining or improving measurement accuracy.
Data Source
AI summary
A method of correlating a representation of a body to a three dimensional representation of the body. A representation (such as a two dimensional image) of a body is acquired and a fitness function is generated for it. A two dimensional outline of the three dimensional representation of the body is then generated for a number of sets of three dimensional position and orientation values. These are compared to the fitness function to generate measures of fit for each set of three dimensional position and orientation values. New sets of position and orientation values are then bred using a genetic algorithm or other breeding algorithm. The method is repeated until the measure of fit converges to an acceptable solution. There is also disclosed a system for performing the method.