Image Registration for Tissue Treatment Under Lighting Variations
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Solution Overview
Problem
Existing image registration algorithms, particularly phase correlation methods, fail to accurately align images with different lighting conditions, such as bright field versus dark field images, leading to unsatisfactory results in tissue treatment applications.
Innovation Solution
A two-phase image registration method is employed, first estimating a rotation angle and then a translation vector, without using log-polar transforms, to align images with different lighting conditions, ensuring accurate transfer of treatment locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If phase correlation method is used for image registration, then translation alignment is achieved with high accuracy, but the method breaks down when images have different lighting conditions
Solution Approach 1:
The image registration process is segmented into two distinct phases: first estimating rotation angle, then estimating translation vector. This segmentation allows each phase to focus on a specific transformation component, improving overall accuracy while maintaining robustness to lighting variations.
Solution Approach 2:
Instead of directly applying phase correlation to find both rotation and translation simultaneously (which fails under lighting changes), the method inverts the approach by first determining rotation through iterative angle testing, then using the corrected orientation for accurate translation estimation.
2Adaptability or versatility
If log-polar transforms are used to handle rotation and scaling, then the phase correlation method can be adapted, but the complexity of the algorithm increases and performance degrades under lighting variations
Solution Approach 1:
The method extracts and handles rotation and translation as separate, independent components. By taking out the rotation estimation as a preliminary step and using it to correct image orientation before translation estimation, the complex joint problem is decomposed into simpler sequential tasks.
Solution Approach 2:
Rotation estimation is performed as a preliminary action before translation estimation. This preliminary correction of rotational misalignment ensures that subsequent translation estimation operates on properly oriented images, improving accuracy without requiring complex log-polar transforms.
3Ease of operation
If traditional image registration is used to transfer treatment locations, then locations can be transferred between images, but accuracy is lost when lighting conditions differ
Solution Approach 1:
The method uses iterative feedback in the rotation estimation phase, where multiple candidate rotation angles are tested and the one providing best alignment is selected. This feedback mechanism ensures accurate orientation correction, which directly improves the precision of subsequent location transfer operations.
Solution Approach 2:
The patent replaces the mechanical assumption of rigid body transformation with a two-stage computational approach that separately handles rotation and translation. This substitution of the transformation model allows for more accurate location transfer under varying lighting conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise transfer of treatment locations from one image to another, even with varying lighting, enhancing the accuracy and efficiency of tissue treatment processes.
Implementation Method 1
the phase correlation method is based on the Fourier shift property, which states that a shift in the coordinate frames of two functions is transformed in the Fourier domain as linear phase differences
Implementation Method 2
one computes the normalized cross power spectrum from the product of the Fourier transforms of the two functions, then computes the inverse Fourier transform, and finally one finds the maximum value
Data Source
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AI summary
Some embodiments are directed to image registration. A rotation angle is determined and subsequently a translation vector. The rotation angle and translation vector together defining an alignment transformation between a first image and a second image. Determining the rotation angle may include iterating over multiple potential rotation angles, and determining alignment.