Tissue Image Registration Across Bright and Dark Field Lighting
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Solution Overview
Problem
Existing image registration algorithms, such as the phase correlation method, fail to accurately align images with different lighting conditions, particularly when registering bright field and dark field images of tissues, 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 between images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the phase correlation method is used for image registration, then the method is robust to various types of noise and provides accurate translation vectors, but the method breaks down when the two images have different lighting conditions
Solution Approach 1:
The patent changes the parameter space by transforming images from Cartesian to log-polar coordinates, which converts translation and rotation operations into translation operations in the transformed space. This parameter transformation allows the phase correlation method to handle both translation and rotation while maintaining robustness to noise and adapting to different lighting conditions through the use of rotation-invariant features
2Adaptability or versatility
If log-polar transforms are used to handle rotation and scaling, then the method can determine rotation and scaling differences between images, but the method becomes more complex and still breaks down with different lighting
Solution Approach 1:
The patent extracts only the essential transformation parameters (translation and rotation) needed for the specific tissue treatment application, rather than implementing a full log-polar transform system. By removing unnecessary complexity and focusing on the critical degree of freedom (rotation angle estimation followed by translation vector determination), the method achieves the required adaptability with reduced algorithmic complexity
3Device complexity
If traditional image registration methods are used, then the processing is relatively simple, but the alignment accuracy is unsatisfactory for tissue treatment applications with different lighting conditions
Solution Approach 1:
The patent performs preliminary action by first estimating the rotation angle between images before determining the translation vector. This sequential approach, where rotation is corrected first and then translation is calculated, preliminary prepares the images for accurate alignment. This two-step preliminary action significantly improves alignment accuracy for tissue treatment applications while maintaining relative algorithmic simplicity
Data Source
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.