3D Image Segmentation Using Adaptive Geodesic Active Contours
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Solution Overview
Problem
Current 3D image segmentation methods for blood vessels and aneurysms face challenges due to complex structures, limited image resolution, and the generation of unwanted artifacts, often requiring manual parameter configuration and being sensitive to noise and contrast variations, leading to sub-optimal performance.
Innovation Solution
The method employs adaptive parameter setting for Geodesic Active Contours (GAC) using shape information to guide gradient mapping, iteratively correcting parameter images to enhance segmentation accuracy, particularly in regions with high appearance variability and noise, and applies multiscale Hessian matrix analysis for shape filtering to improve vessel segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D image segmentation methods are used, then segmentation can be performed, but segmentation accuracy is sub-optimal due to complex structures, limited image resolution, and noise sensitivity
Solution Approach 1:
The patent applies local quality by using adaptive parameter setting where different parameter values are assigned to different regions of the 3D image based on local characteristics. The Hessian matrix analysis is performed locally at each voxel to determine tube-like, blob-like, or sheet-like structures, allowing the segmentation to adapt to local anatomical variations and improve accuracy while maintaining robustness to noise.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting segmentation parameters based on local image characteristics. The adaptive parameter setting modifies parameters such as the speed function in the level set equation based on the Hessian matrix eigenvalues and eigenvectors, allowing the segmentation process to respond to varying local conditions and improve overall segmentation accuracy.
2Measurement precision
If manual parameter configuration is required, then segmentation can be optimized for specific cases, but configuration effort and time increase significantly
Solution Approach 1:
The patent applies self-service by implementing automated adaptive parameter setting that performs Hessian matrix analysis and automatically determines optimal parameters for each region. This eliminates the need for manual parameter configuration by users, as the system autonomously adapts parameters based on local image characteristics, significantly reducing configuration effort while maintaining high segmentation accuracy.
Solution Approach 2:
The patent implements preliminary action by pre-computing the Hessian matrix and its eigenvalues/eigenvectors for all voxels before the actual segmentation process. This preliminary analysis of image structure allows the system to have parameter values ready for adaptive adjustment during segmentation, eliminating the need for time-consuming manual configuration while maintaining optimization.
3Ease of operation
If a single global configuration is applied to all voxels, then configuration is simplified, but segmentation performance becomes sub-optimal in regions with high appearance variability
Solution Approach 1:
The patent resolves this contradiction by implementing local quality through adaptive parameter setting where parameters vary spatially based on local image characteristics determined by Hessian matrix analysis. Each voxel's parameters are adjusted according to its local structure (tube-like, blob-like, or sheet-like), maintaining configuration simplicity from the user perspective while achieving high segmentation accuracy in regions with high appearance variability.
4Shape
If enhancement filters based on second-order derivatives are used, then vessel structures can be distinguished, but results become sensitive to noise and contrast variations
Solution Approach 1:
The patent applies parameter changes by using the Hessian matrix eigenvalues and eigenvectors to dynamically adjust segmentation parameters based on local structural characteristics. Rather than using fixed enhancement filters, the system adapts parameters according to the computed Hessian properties at each location, improving vessel structure identification while reducing sensitivity to noise and contrast variations through localized adaptation.
Data Source
AI summary
A method and apparatus for automated comparison of 3D images, such as specific shapes in images, for example, for detecting vascular changes and aneurysm growth. The method comprises an adaptive geodesic active contour (GAC) approach which can be performed in a single pass, or across multiple iterations. The method uniquely utilizes shape to identify targets in two 3D images so that images can be compared if there is any shape feature. A method is also described for iterative parameter image construction, which beneficially removes false positives. The technology is particularly well-suited for use in comparing geometric changes of aneurysms, tumors, thromboses, inflammations.