Vessel Centerline Detection Using Image Transformation and Level Set Algorithms
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Solution Overview
Problem
Current methods for detecting the centerline of a vessel in medical imaging technologies, such as MRI, lack precision and efficiency, particularly in accurately tracing the centerline of vessels with complex structures like bifurcations and varying diameters.
Innovation Solution
A method involving image transformation techniques, including grayscale and distance field transformations, combined with level set algorithms, to identify and track the endpoints of vessels, and determine the centerline by calculating crosspoints and gradient descent, enhancing the detection of vessel centerlines in complex anatomical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection methods are used for vessel centerline, then the detection process is simple, but the detection precision is insufficient
Solution Approach 1:
The patent segments the vessel centerline detection into multiple stages: initial centerline acquisition, bifurcation point detection, and separate centerline tracing for each vessel branch. This segmentation allows complex vessels with bifurcations to be handled systematically, improving detection precision without overwhelming complexity
Solution Approach 2:
The patent performs preliminary actions by first acquiring an initial centerline and identifying bifurcation points before tracing individual vessel branches. This preliminary preparation enables more accurate detection by establishing a framework for subsequent detailed analysis of complex vessel structures
2Adaptability or versatility
If simple detection methods are used, then the processing speed is fast, but the ability to handle complex vessel structures is poor
Solution Approach 1:
The patent employs dynamic adaptation by detecting bifurcation points and adjusting the detection path accordingly. The method dynamically switches between tracing single vessels and handling bifurcations, allowing the system to adapt to varying vessel complexities while maintaining efficient processing through automated decision-making
3Productivity
If manual detection methods are used, then flexibility is high, but productivity is low
Solution Approach 1:
The patent implements self-service through automated bifurcation point detection and automatic determination of detection paths. The system independently identifies complex vessel structures and adjusts its detection strategy without manual intervention, significantly improving productivity while maintaining operational simplicity through automated decision-making processes
Data Source
AI summary
This application disclosures a method and system for detecting a centerline of a vessel. The method may include obtaining image data, wherein the image data may include vessel data; selecting two endpoints of the vessel based on the vessel data; transforming the image data to generate a transformed image based on at least one image transformation function; and determining a path of the centerline of the vessel connecting the first endpoint of the vessel and the second endpoint of the vessel to obtain the centerline of the vessel based on the transformed image. The two endpoints of the vessel may include a first endpoint of the vessel and a second endpoint of the vessel.