3D Telestration via Sparse Feature Matching
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Solution Overview
Problem
Existing image matching methods for telestration in surgical fields face challenges due to sparse and unreliable image features, specular reflections, and real-time processing requirements, leading to less than ideal results in complex surgical environments.
Innovation Solution
The method involves selectively identifying and matching points of interest in one image to another, using techniques like region matching, feature matching, and interpolation, without generating a disparity map, to provide robust and efficient 3-D telestration, even in poorly defined regions with sparse textures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image matching methods are used to match all pixels in surgical images, then comprehensive image alignment is achieved, but computational complexity increases and real-time processing becomes difficult
Solution Approach 1:
The patent segments the image matching process into two stages: first matching a small number of sparse feature points (landmarks) between stereo images, then using these matched points to define regions of interest (ROIs) that are subsequently matched. This segmentation reduces the overall computational burden while maintaining matching reliability in critical surgical areas.
Solution Approach 2:
Instead of matching all pixels across the entire surgical image, the patent applies partial action by selectively matching only the most critical elements: sparse feature points and their surrounding ROIs. This partial matching approach achieves sufficient reliability for surgical telestration without the excessive computational cost of complete image matching.
2Productivity
If sparse image features are used in surgical fields, then processing speed increases, but matching accuracy decreases due to insufficient features
Solution Approach 1:
The patent applies local quality by treating different regions of the surgical image differently: sparse feature points are matched with high precision using feature-based methods, while surrounding regions are matched using the ROI approach. This allows the system to maintain high matching accuracy in critical local areas while preserving overall processing speed.
Solution Approach 2:
The patent performs preliminary action by first identifying and matching sparse feature points before proceeding to match surrounding regions. This preliminary feature matching establishes reliable correspondence relationships that guide subsequent ROI matching, ensuring both speed and accuracy in the overall process.
3Loss of information
If disparity maps are generated for 3-D telestration, then complete depth information is obtained, but computational time increases significantly
Solution Approach 1:
The patent extracts only the essential depth information needed for surgical telestration by matching sparse feature points and their surrounding ROIs, rather than generating complete disparity maps for the entire image. This extraction approach obtains sufficient depth information for clinical purposes while dramatically reducing computational time.
Solution Approach 2:
Instead of performing the excessive action of generating complete disparity maps, the patent applies partial action by computing depth information only for matched feature points and their surrounding ROIs. This partial depth computation maintains adequate 3-D telestration quality while minimizing computational time loss.
Data Source
AI summary
An apparatus is configured to show telestration in 3-D to a surgeon in real time. A proctor is shown one side of a stereo image pair, such that the proctor can draw a telestration line on the one side with an input device. Points of interest are identified for matching to the other side of the stereo image pair. In response to the identified points of interest, regions and features are identified and used to match the points of interest to the other side. Regions can be used to match the points of interest. Features of the first image can be matched to the second image and used to match the points of interest to the second image, for example when the confidence scores for the regions are below a threshold value. Constraints can be used to evaluate the matched points of interest, for example by excluding bad points.