3D Image Slice Alignment Using Anatomical Edges and Silhouettes
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Solution Overview
Problem
Existing methods for aligning 3D image data with a person's anatomical structure in augmented reality (AR) headsets are inaccurate and cumbersome, particularly due to difficulties in determining depth alignment and obscuration of anatomical structures by bright projected images.
Innovation Solution
The method involves identifying pre-defined or user-selected axes, locking a slice of the 3D image data orthogonal to these axes, and using sensors or user input to align the edge of the slice with the anatomical structure, employing techniques like pattern matching and machine learning to ensure accurate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If 3D image data is projected with high brightness for clear visibility, then visibility of the image is improved, but the anatomical structure becomes obscured
Solution Approach 1:
The alignment process is segmented into multiple stages: initial rough alignment using low-brightness or transparent projection, followed by precise edge alignment using silhouette detection. This allows the anatomical structure to remain visible during critical alignment phases while still providing adequate image visibility when needed.
Solution Approach 2:
The system performs preliminary alignment using edge detection and silhouette matching before finalizing the high-brightness projection. By pre-aligning the 3D image data to the anatomical structure using sensor data and pattern matching, the system ensures accurate positioning is achieved before the bright projection begins, preventing obscuration issues.
2Ease of operation
If manual adjustment of 3D image data is allowed for alignment, then ease of operation is improved, but alignment precision deteriorates due to difficulty in judging depth
Solution Approach 1:
The system provides real-time feedback during manual adjustment by displaying alignment indicators, edge overlap metrics, and silhouette matching quality. Users can see quantitative and visual feedback about their alignment progress, enabling them to make precise depth adjustments based on objective criteria rather than subjective judgment alone.
Solution Approach 2:
The system replaces purely manual mechanical adjustment with a hybrid approach where sensor data (optical trackers, depth sensors) and computer vision algorithms assist the manual positioning. The system uses pattern matching and edge detection to guide and verify alignment, substituting subjective depth judgment with objective measurement feedback.
3Measurement precision
If automated alignment methods are used to improve precision, then alignment accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs automated edge detection, silhouette extraction, and alignment verification using the AR headset's built-in sensors and processing capabilities. The device uses its own optical trackers, depth sensors, and image processing algorithms to automatically align the 3D image data without requiring external complex alignment equipment or manual intervention for the computationally intensive tasks.
Data Source
AI summary
Technology is described for aligning 3D image data with an anatomical structure of a person. The method can include displaying a slice of the 3D image data with respect to an axis. The slice may be locked to limit movement of the slice with respect to the axis. Adjustments to the slice with respect the axis may be received until an edge of the anatomical structure in the slice aligns with an edge of the anatomical structure of the person. The adjustments may allow the 3D image data to align with the person.