3D Mesh Model Resolution Adaptation for Medical Image Segmentation
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Solution Overview
Problem
Existing methods for fitting 3-D mesh models onto complex medical image surfaces face challenges such as self-intersections and inadequate resolution, particularly in areas with noisy or distant image features, leading to incorrect segmentation and computational inefficiencies.
Innovation Solution
The proposed system dynamically adapts the resolution of the 3-D Simplex Mesh Model based on feature confidence parameters, increasing resolution where reliable features are present and maintaining a coarse resolution in noisy or feature-poor areas, preventing self-intersections and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the 3-D mesh model uses a fixed high resolution throughout, then the segmentation precision is improved, but the computational time and processing complexity increase significantly
Solution Approach 1:
The patent implements local quality by dynamically adjusting the mesh resolution at different locations based on feature confidence parameters. Areas with high feature confidence (reliable image features) receive high resolution for precise segmentation, while areas with low feature confidence (noisy or distant regions) maintain coarse resolution to reduce computational load. This spatially varying resolution strategy resolves the contradiction between overall precision and computational efficiency.
Solution Approach 2:
The patent applies dynamics by making the mesh resolution adaptive rather than static. The resolution of each mesh element is dynamically adjusted during the fitting process based on the confidence parameters of detected image features. This dynamic resolution adjustment allows the system to automatically allocate computational resources where they are most needed, balancing precision and processing time throughout the segmentation task.
2Measurement precision
If the mesh model is forced to fit complex surfaces with high resolution, then the segmentation accuracy is improved, but the model may self-intersect and produce incorrect results
Solution Approach 1:
The patent uses local quality to prevent self-intersections by applying high resolution only where image features provide reliable guidance. In regions with low feature confidence, the coarse resolution prevents the mesh from attempting to fit noisy or distant features, thereby avoiding self-intersections and invalid geometries. This localized approach ensures that high-resolution fitting occurs only where it can produce reliable, valid results.
Solution Approach 2:
The patent implements feedback through the confidence parameter estimation process. The system continuously monitors the quality of image features and adjusts mesh resolution accordingly. When feature confidence is low, the system reduces resolution to prevent invalid fitting; when confidence is high, it increases resolution for accurate segmentation. This feedback mechanism ensures model validity while maintaining segmentation accuracy.
3Device complexity
If the mesh model uses uniform resolution, then the device complexity is simplified, but it cannot adapt to areas with noisy or distant image features
Solution Approach 1:
The patent resolves this contradiction by transitioning from uniform to non-uniform resolution. Each mesh element's resolution is locally adjusted based on the confidence parameters of associated image features. This allows the mesh to adapt its complexity to local conditions, maintaining simplicity where features are unreliable and increasing complexity where features are reliable, thereby improving adaptation reliability without unnecessarily increasing overall device complexity.
4Ease of manufacture
If the system processes all image features equally, then the processing procedure is simplified, but it wastes computational resources on noisy or distant features
Solution Approach 1:
The patent applies local quality to computational processing by assigning different processing weights to different image features based on their confidence parameters. Features with high confidence (reliable, close features) receive high processing weight and drive high-resolution mesh fitting, while features with low confidence (noisy, distant features) receive reduced weight and contribute to coarse-resolution fitting. This selective processing strategy improves computational efficiency by focusing resources on informative features.
Solution Approach 2:
The patent uses partial action by processing only the essential features needed for accurate segmentation. Instead of treating all features equally, the system identifies and processes only the reliable, informative features (partial set) while disregarding or minimizing processing of noisy or distant features (excessive action avoided). This partial processing approach maintains procedure simplicity while significantly improving computational efficiency.
Data Source
AI summary
An image processing system having means of automatic adaptation of 3-D surface Model to image features, for Model-based image segmentation, comprising: dynamic adaptation means for adapting the Model resolution to image features including locally setting higher resolution when reliable image features are found and setting lower resolution in the opposite case. This system comprises estimation means for estimating a feature confidence parameter for each image feature. The model resolution is locally adapted according to said parameter. The feature confidence parameter depends on the feature distance and on the estimation of quality of this feature including estimation of noise. The large distances and the noisy, although close features are penalized. The resolution of the Model is decreased in absence of confidence and is gradually increased with the rise of feature confidence.