3D Surgical Marker Posture Compensation for Real-Time Positioning
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Solution Overview
Problem
Existing three-dimensional real-time positioning methods for surgery suffer from significant errors due to pixel-based image processing, leading to inaccurate calculations of the posture of three-dimensional marking devices, which affects surgical precision.
Innovation Solution
Increase the number of sampling points and correct the posture of the marking devices using image brightness normalization and fuzzy logic operations to minimize errors, employing a deep learning model for object detection and projection transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pixel-based image processing is used to detect marking device positions, then the positioning process is simple and fast, but the measurement precision deteriorates due to ±3 pixel errors at corner points
Solution Approach 1:
The patent segments the marking device detection process into multiple independent stages: initial coarse detection using corner points, brightness normalization, texture image generation at multiple resolutions, and iterative optimization. This segmentation allows each stage to focus on specific aspects of precision without compromising overall processing efficiency.
Solution Approach 2:
The patent applies partial action by using only N selected sampling points (where N < total pixels) from the texture images for optimization, rather than processing all pixels. This selective sampling maintains measurement precision while avoiding the computational burden of exhaustive pixel-level processing.
2Device complexity
If only four corner points are used for projection transformation, then the calculation is simple and fast, but the manufacturing precision deteriorates due to large errors in posture calculation
Solution Approach 1:
The patent transitions from two-dimensional corner point coordinates to three-dimensional texture image data with multiple resolutions. By generating texture images at different resolutions and selecting N sampling points from these multi-dimensional data structures, the system achieves more accurate posture calculation while maintaining manageable computational complexity through systematic dimensionality management.
Solution Approach 2:
The patent performs preliminary brightness normalization and texture image generation before the actual posture optimization calculation. This preliminary processing prepares the data in advance, ensuring that when N sampling points are selected and used for optimization, the calculations start with pre-processed, high-quality data that reduces the complexity of subsequent precision-critical computations.
3Device complexity
If image brightness variations are not corrected, then the processing is simpler, but the measurement precision deteriorates due to errors in posture calculation
Solution Approach 1:
Brightness normalization is performed as a preliminary step before texture image generation and sampling point selection. This advance correction of brightness variations ensures that all subsequent processing stages work with uniformly illuminated images, eliminating brightness-related errors from the outset without adding complexity to the main processing pipeline.
Solution Approach 2:
The patent introduces texture images at multiple resolutions as an intermediary representation between the original surgical image and the final posture calculation. These texture images serve as a mediator that captures essential visual information while reducing the impact of brightness variations and image gradients, allowing accurate posture measurement without directly processing problematic pixel-level data.
Data Source
AI summary
A three-dimensional real-time positioning compensation method for surgery includes: obtaining an initial posture of each of a plurality of marking devices in a surgical image in a world coordinate system; performing brightness normalization on the surgical image to generate a normalized image; respectively generating a plurality of texture images with different resolutions for all marks of the plurality of marking devices, and using the texture image having the closest resolution as a standard image; selecting at least N additional sampling points from the standard image, and obtaining a corresponding reference point in the normalized image; and obtaining an optimization point with a minimum brightness error from each reference point, respectively performing a calculation to obtain an error; and correcting the initial posture according to the errors to generate a compensation posture.