Left Ventricle Detection in Echocardiograms Using Pose-Estimated Classifier Cascades
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Solution Overview
Problem
Current object detection systems in medical imaging, such as echocardiograms, face inefficiencies in detecting the left ventricle due to variations in size, orientation, and angle, leading to complex classifiers and increased processing time.
Innovation Solution
A method employing a multi-class pose classifier and serially-linked binary object feature classifiers to estimate the pose of the left ventricle, dividing the image into subwindows, and selecting cascades for detection based on estimated pose to improve detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current object detection systems are used to detect the left ventricle in echocardiograms, then detection capability is provided, but processing time increases and accuracy decreases due to variations in size, orientation, and angle
Solution Approach 1:
The patent divides the image processing task into multiple cascaded stages, where each stage processes only relevant regions based on detections from previous stages. This segmentation allows the system to focus computational resources on promising areas rather than exhaustively searching the entire image, thereby reducing processing time while maintaining detection accuracy across various orientations and scales.
Solution Approach 2:
The patent performs preliminary pose estimation and candidate region identification before conducting detailed object detection. By pre-identifying potential locations and orientations of the left ventricle based on coarse features, the system eliminates the need for exhaustive search across all possible poses, significantly reducing processing time while preserving detection accuracy.
2Reliability
If exhaustive sliding window search is performed to detect the left ventricle, then detection completeness is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the exhaustive search space into multiple cascaded detection stages, where each stage handles a subset of candidate regions. Early stages filter out obvious non-matches, while later stages perform detailed analysis on fewer refined candidates. This segmentation maintains detection completeness by ensuring all potential targets are eventually evaluated, while improving processing speed by avoiding redundant computations on clearly negative regions.
Solution Approach 2:
The patent applies partial action by performing exhaustive search only on candidate regions identified by preliminary estimators, rather than across the entire image space. This selective application of exhaustive search maintains reliability for detecting all instances of the left ventricle while significantly improving productivity by limiting the search domain to plausible candidates only.
3Measurement precision
If multiple classifiers are trained for different orientations and scales, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the classifier system into a hierarchical structure with a pose estimation classifier at the top level and orientation-specific detection classifiers at lower levels. This segmentation allows the system to use a single, simpler pose estimation model to identify candidate regions, then apply only the necessary orientation-specific classifiers to those candidates. This reduces overall complexity compared to training and maintaining multiple parallel classifiers for all orientations, while preserving detection accuracy through selective application of specialized classifiers.
Solution Approach 2:
The patent performs preliminary pose estimation to determine the likely orientation and scale of the target object before selecting and applying specific classifiers. This preliminary action allows the system to choose only the relevant classifiers needed for the detected pose, rather than maintaining and applying all possible orientation-specific classifiers. This reduces device complexity by dynamically selecting a subset of classifiers based on preliminary analysis, while maintaining high detection accuracy through pose-adaptive classifier selection.
Data Source
AI summary
A method for detecting an object of interest in an input image includes the computer-implemented steps of: receiving an image, providing a multi-class pose classifier that identifies a plurality of pose features for estimating a pose of the object of interest, providing a plurality of cascades of serially-linked binary object feature classifiers, each cascade corresponding to different poses of the object of interest in the input image, selecting at least one of the cascades using the estimated pose, and employing the selected cascades to detect instances of the object of interest in the image.