Classifier Pose Initialization for 3D TEE Segmentation
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Solution Overview
Problem
In medical imaging, particularly in 3D TEE, the variability in anatomical structures and imaging geometry leads to unreliable image segmentation due to unknown or changing poses of objects, such as the heart, which existing technologies fail to address effectively.
Innovation Solution
An image processing apparatus utilizing a generalized Hough transform to determine the pose of objects within images, allowing for automatic initialization of segmentation algorithms, even in scenarios with varying poses and orientations, by using pre-defined poses and transformations to adapt geometric models to the image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard initialization based on prior knowledge is used, then the segmentation process is simple, but it fails when anatomical variability or changing imaging geometry causes pose changes
Solution Approach 1:
The system performs preliminary pose estimation using Generalized Hough Transform before segmentation. Pre-defined poses are prepared in advance, and the GHT algorithm detects the actual pose of anatomical structures in the input image, outputting pose parameters that are used to initialize the segmentation process. This preliminary action ensures reliable initialization even when anatomical variability or imaging geometry changes occur.
2Measurement precision
If manual pose determination is used, then accuracy can be ensured, but automation is reduced and time consumption increases
Solution Approach 1:
The system enables automatic pose detection through the Generalized Hough Transform algorithm. The classifier automatically processes the input image, compares it with pre-defined poses, and determines the object pose without manual intervention. The GHT algorithm self-identifies the pose parameters by matching image features with pre-defined pose templates, achieving both high automation and accurate pose determination.
3Productivity
If pre-defined poses are used for classification, then the classification process is efficient, but it may not cover all anatomical variations
Solution Approach 1:
The system uses a dynamic pose classification approach where multiple pre-defined poses represent different anatomical configurations. The Generalized Hough Transform dynamically selects the most appropriate pose from the pre-defined set by comparing image features with each pose template. This allows the system to adapt to various anatomical variations while maintaining efficient classification through pre-computed pose templates.
Data Source
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AI summary
An image processing apparatus and related method. The apparatus (PP) comprises an input port (IN), a classifier (CLS) and an output port (OUT). The input port is capable of receiving an image of an object acquired at a field of view (FoV) by an imager (USP). The image records a pose of the object corresponding to the imager's field of view (FoV). The classifier (CLA) is configured to use a geometric model of the object to determine, from a collection of pre-defined candidate poses, the pose of the object as recorded in the image. The output port (OUT) is configured to output pose parameters descriptive of the determined pose.