Cardiac Chamber Segmentation via Adjustable Edge Models
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Solution Overview
Problem
Current image processing techniques for cardiac chamber segmentation in medical imaging face challenges in accurately identifying and matching cardiac chamber edges, particularly in varying anatomical structures, which hampers precise diagnosis and treatment planning.
Innovation Solution
A method involving image data processing, model reconstruction, and edge matching using a weighted Generalized Hough Transform, where a model with adjustable edges is matched to the image data, utilizing classifiers to determine probability-based correlations for precise edge adjustments, enabling accurate segmentation of cardiac chambers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a variable model is used for cardiac chamber segmentation, then the segmentation can be performed using average image data of multiple clinical models, but the accuracy of identifying cardiac chamber edges in varying anatomical structures deteriorates
Solution Approach 1:
The patent applies dynamics by making the model edges adjustable and adaptable to individual patient anatomy. The system starts with a variable model containing adjustable edges and modifies these edges based on the specific anatomical structures in the patient's images, allowing the model to dynamically adapt from generic average data to precise patient-specific segmentation.
Solution Approach 2:
The patent changes parameters by adjusting the positions and configurations of model edges based on image data analysis. The system modifies edge parameters (positions, orientations) to match the actual cardiac chamber boundaries in the patient's images, transforming the model from static average data to a customized segmentation template.
2Measurement precision
If model edges are adjusted to match image edges, then segmentation accuracy improves, but the complexity of the matching process increases
Solution Approach 1:
The patent replaces manual or mechanical edge-matching processes with automated image processing and pattern recognition algorithms. The system uses computer-based methods to automatically compare model edges with image edges, calculate deviations, and adjust model parameters, substituting complex mechanical adjustment procedures with computational algorithms.
Solution Approach 2:
The patent uses copying by creating a replicable variable model template that can be applied to multiple patients. The model containing adjustable edges serves as a reusable template that can be copied and adapted to different patients' images, standardizing the segmentation process while maintaining accuracy through parameter adjustment.
3Adaptability or versatility
If a variable model with adjustable edges is used, then adaptability to different anatomical structures improves, but the time required for model adjustment increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring the variable model with adjustable edges and pre-processing the image data before actual segmentation. The system prepares the model template in advance with all necessary adjustment parameters and pre-processes patient images to facilitate rapid matching, reducing the time required during the actual segmentation process.
Solution Approach 2:
The patent replaces time-consuming manual model adjustment with automated computational algorithms that rapidly calculate optimal edge positions and model parameters. The system uses image processing algorithms to automatically match model edges with anatomical structures, substituting slow manual adjustment procedures with fast computer-based optimization.
Data Source
AI summary
The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.