Doppler Image Shape Similarity for Automated Cardiac Diagnosis
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Solution Overview
Problem
Current medical diagnosis methods rely on single-sample analysis, which is inadequate for complex disease conditions, and manual tracing of Doppler images is time-consuming and prone to errors, lacking automated tools for pattern recognition and correlation with diseases.
Innovation Solution
A method and system for processing Doppler images using shape-based similarity matching, where a processor extracts envelope curves from query and stored images, matches features, and retrieves images from a database to identify diseases, employing pre-processing, velocity envelope extraction, segmentation, and shape-based dynamic time warping for robust and invariant matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracing of Doppler images is used to extract measurements, then measurement precision can be achieved, but time consumption increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical tracing with automated computer-based envelope curve extraction algorithms. The system automatically extracts velocity envelope curves from Doppler images using image processing techniques, eliminating the need for manual tracing while maintaining measurement precision and significantly improving productivity.
Solution Approach 2:
The system enables self-service automated analysis where the Doppler image processing system independently extracts measurements without requiring manual intervention. The automated envelope curve extraction and disease pattern recognition algorithms perform the analysis autonomously, allowing the system to serve itself rather than requiring continuous manual operation.
2Ease of operation
If single-sample-guided diagnosis methodology is used, then diagnostic simplicity is maintained, but diagnostic accuracy deteriorates for complex disease conditions
Solution Approach 1:
The patent segments the diagnostic process into multiple independent analysis components: envelope curve extraction, feature extraction, shape-based similarity matching, and disease pattern recognition. This segmentation allows complex disease conditions to be analyzed through multiple independent algorithms working in parallel, improving diagnostic accuracy while maintaining operational simplicity through modular architecture.
Solution Approach 2:
The system implements a universal automated analysis platform that can handle multiple disease types and complex disease combinations through a single integrated framework. The shape-based similarity matching algorithm universally applies to various valve diseases and cardiac abnormalities, providing multi-functional diagnostic capability without increasing operational complexity.
3Productivity
If automated pattern recognition is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The patent extracts and isolates specific diagnostic features from the complex Doppler images, such as envelope curve shapes and characteristic patterns. By taking out only the essential features needed for disease identification and separating them from the rest of the image data, the system achieves automated pattern recognition without requiring complex analysis of all image components.
Solution Approach 2:
The system achieves equipotentiality by using shape-based similarity matching that is invariant to heart rate and signal intensity variations. This allows the automated recognition algorithm to work equally well across different patients and conditions without requiring complex normalization or adjustment procedures, simplifying the device while maintaining high productivity.
Data Source
AI summary
Continuous wave Doppler images in a data base comprising cardiac echo studies are processed to separate Doppler frames. The frames are pre-processed to extract envelope curves and their corner shape features. Shape patterns in Doppler images from echo studies of patients with known cardiac (valvular) diseases are employed to infer the similarity in valvular disease labels for purposes of automated clinical decision support. Specifically, similarity in appearance of Doppler images from the same disease class is modeled as a constrained non-rigid translation transform of velocity envelopes embedded in these images. Shape similarity between two Doppler images is then judged by recovering the alignment transform using a variant of dynamic shape warping. Since different diseases appear as characteristic shape patterns in Doppler images, measuring the similarity in the shape pattern conveyed within the velocity region of two Doppler images can infer the similarity in their diagnosis labels.