2D-3D Image Registration via Mean Shift Sampling
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Solution Overview
Problem
Current 2D-3D image registration methods face limitations in robustness due to reliance on 'off-the-shelf' optimization techniques, which struggle with local non-convexities and have a limited capture range, especially in intensity-based approaches that require excessive cost function calls, leading to slow computation.
Innovation Solution
A method that reformulates 2D-3D registration as a random exploration of the search manifold using a robust classification scheme, such as the mean shift algorithm, to iteratively estimate local characteristics and update registration parameters, enhancing capture range and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional optimization techniques are used for 2D-3D registration, then the method is simple to implement, but the capture range is limited and the method is sensitive to local non-convexities
Solution Approach 1:
The patent segments the registration process into two distinct phases: (1) a global optimization phase using Monte Carlo sampling to explore the entire parameter space and identify the basin of attraction, and (2) a local refinement phase using gradient-based optimization to precisely converge to the optimal solution. This segmentation allows the method to overcome local non-convexities in the first phase while maintaining computational efficiency in the second phase, thereby resolving the contradiction between simplicity and robustness.
Solution Approach 2:
The patent performs preliminary action by conducting a global Monte Carlo search before the local optimization step. This preliminary exploration identifies the correct basin of attraction and provides an initial guess that is guaranteed to be in the correct region, preventing convergence to local minima. This preliminary action ensures robustness without requiring the entire process to be computationally intensive.
2Measurement precision
If intensity-based methods are used for registration, then the entire image can be utilized, but excessive cost function calls are required leading to slow computation
Solution Approach 1:
The patent implements periodic action by alternating between two types of operations: (1) periodic Monte Carlo sampling steps that perform coarse exploration of the parameter space, and (2) periodic gradient-based refinement steps that perform precise local optimization. This periodic alternation allows the method to maintain high accuracy through intensive local refinement while controlling computation time by limiting the number of expensive cost function evaluations to only what is necessary for global exploration.
Solution Approach 2:
The patent changes parameters dynamically by adjusting the step size and sampling density based on the current stage of optimization. During the global Monte Carlo phase, larger parameter changes are permitted to explore distant regions efficiently. During the local refinement phase, parameter changes are restricted to small increments around the current estimate, reducing the number of required evaluations while maintaining precision. This adaptive parameter change resolves the contradiction between accuracy and speed.
Data Source
AI summary
A method for registering 2-dimensional (2D) images with 3-dimensional (3D) images includes receiving a 2D reference image and a 3D moving image, initializing a registration parameter matrix that rigidly transforms the domain of the moving image, randomly sampling a set of registration parameter matrices in a neighborhood of the initial registration parameters, estimating a cost function for each of the randomly sampled parameter matrices, calculating a distance from each randomly sampled parameter matrix to the initial registration parameter matrix, calculating a mean shift vector from the estimated cost functions and distance, and updating the initial registration parameter matrix from the mean shift vector.