3D Medical Image Curvature Analysis for Local Deformation Prediction
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Solution Overview
Problem
Existing 3D image processing methods for disease diagnosis fail to effectively quantify local pathological deformations, leading to insufficient accuracy in disease prediction due to incomplete utilization of 3D morphological information and reliance on single scalar values or high-dimensional features.
Innovation Solution
Extract and analyze the distribution of curvatures from reconstructed 3D meshes of anatomical structures in medical images to characterize local deformations, using supervised machine learning models like Random Forest or XGboost for precise disease severity prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D image processing methods are used for disease diagnosis, then the processing is simple and fast, but the accuracy of disease prediction is insufficient due to inability to effectively quantify local pathological deformations
Solution Approach 1:
The patent segments the 3D mesh surface into multiple local regions and calculates curvature values for each region separately. This segmentation allows the system to capture local pathological deformations with high precision while maintaining a structured processing approach that manages complexity through systematic division of the analysis task.
Solution Approach 2:
The patent transitions from analyzing simple scalar values to analyzing the distribution of curvature values across the 3D surface. By introducing the dimensional aspect of curvature distribution (min, max, mean values across different regions), the system achieves more accurate disease prediction without excessive complexity increase.
2Measurement precision
If single scalar values or high-dimensional features are used to represent 3D morphological information, then the data processing is simplified, but the quantification of local pathological deformations becomes insufficient
Solution Approach 1:
The patent applies local quality by calculating curvature values specifically for different regions of the 3D mesh surface rather than using a single global value. This allows the system to capture local pathological deformations with high precision, distinguishing between different areas of the anatomical structure to identify region-specific abnormalities.
Solution Approach 2:
The patent calculates multiple curvature statistics (minimum, maximum, mean) for different regions, which represents a partial approach to full high-dimensional analysis. This selective extraction of key curvature features provides sufficient deformation quantification without the excessive complexity of complete high-dimensional feature sets.
3Reliability
If comprehensive 3D morphological information is utilized for disease diagnosis, then the prediction accuracy improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent extracts specific curvature distribution features (min, max, mean values) from the comprehensive 3D morphological information. This extraction approach allows the system to utilize the most relevant aspects of 3D morphology for improving prediction reliability while avoiding the unnecessary complexity of processing all possible morphological parameters.
Solution Approach 2:
The patent changes the parameter representation from simple scalar values to curvature distribution statistics across multiple regions. This parameter transformation enables more reliable disease prediction by capturing the essence of 3D morphological variations while maintaining a manageable computational framework through focused statistical analysis.
Data Source
AI summary
An apparatus and method for training and using a computing operation for digital image processing are provided. The apparatus and method may be used for 3-dimensional medical images. An exemplary method for digital image processing comprises: receiving an image displaying at least one detectable structure, determining the detectable structure; segmenting the image to obtain a segmentation mask that is associated with a geometric shape and comprises at least one quantifiable visual feature; generating a mesh based on the quantifiable visual feature; computing at least on quantifiable visual parameter based on the mesh; extracting quantifiable visual data from the image based on the quantifiable visual parameter; training the computing operation with the quantifiable visual data. The method for digital image processing further comprises: receiving another image; segmenting, generating a mesh, computing quantifiable visual parameters, and extracting quantifiable visual data; and classifying the extracted quantifiable visual data with the trained computing operation.