Echocardiogram View Classification Using Edge Filtered Motion Features
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Solution Overview
Problem
Current methods for classifying echocardiogram videos are manual, prone to noise and low contrast, and require expert intervention, making automatic viewpoint classification challenging, especially when images can be translated, rotated, or zoomed, and varying pathologies and sonographer expertise complicate the process.
Innovation Solution
A method that aligns echocardiogram videos, generates motion magnitude images, filters them using edge maps, detects scale-invariant features near intensity edges, encodes these features with x, y coordinates and histograms, and uses these encoded features for classification, employing a vocabulary-based PMK and multiclass SVMs for accurate viewpoint classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used for transducer positioning and viewpoint capture, then interpretation accuracy is improved, but productivity deteriorates due to manual labor requirements
Solution Approach 1:
The patent replaces manual mechanical operations (transducer positioning and viewpoint capture by sonographers) with an automated computer vision system that uses image processing algorithms to detect and classify echocardiogram viewpoints automatically, eliminating the need for manual intervention while maintaining accuracy
Solution Approach 2:
The system performs self-service by automatically detecting anatomical structures, determining probe viewpoints, and classifying images without requiring expert sonographers or cardiologists to manually annotate or interpret each frame, allowing the system to process data independently
2Measurement precision
If manual labeling is used for training data, then classification accuracy is improved, but loss of time increases due to expert intervention requirements
Solution Approach 1:
The system automatically generates training data by detecting anatomical structures and determining probe viewpoints without requiring expert annotators to manually label each image, thereby eliminating time-consuming manual labeling while maintaining high classification accuracy through automated feature extraction
Solution Approach 2:
The patent performs preliminary automated detection and extraction of anatomical structures and motion features before classification, creating pre-processed training data that eliminates the need for time-consuming manual annotation steps while preserving the accuracy that would otherwise require expert intervention
3Measurement precision
If edge filtered motion magnitude features are used, then recognition rate is improved, but device complexity increases due to multiple processing steps
Solution Approach 1:
The patent segments the complex image processing task into distinct modular steps: motion magnitude calculation, edge detection, feature extraction, and classification. This segmentation allows each step to be optimized independently and simplifies the overall system architecture while achieving high recognition rates through the combination of these specialized processing stages
Data Source
AI summary
According to one embodiment of the present invention, a method for echocardiogram view classification is provided. According to one embodiment of the present invention, a method comprises: obtaining a plurality of video images of a subject; aligning the plurality images; using the aligned images to generate a motion magnitude image; filtering the motion magnitude image using an edge map on image intensity; detecting features on the motion magnitude image, retaining only those features which lie in the neighborhood of intensity edges; encoding the remaining features by generating, x, y image coordinates, a motion magnitude histogram in a window around the feature point, and a histogram of intensity values near the feature point; and using the encoded features to classify the video images of the subject into a predetermined classification.