3D Medical Image Segmentation for Noise-Reduced Visualization
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Solution Overview
Problem
Existing medical visualization systems for image-guided surgery struggle with noise and background clutter, particularly in 3D models, which can obscure the relevant anatomical structures and hardware, leading to reduced image quality and user experience.
Innovation Solution
A system and method that employs automatic image segmentation to differentiate regions of interest from background, using multiple intensity thresholds to generate 3D models with reduced noise and enhanced visualization, allowing selective display of bone structures and hardware while suppressing soft tissue and other irrelevant features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D models are displayed with background features (soft tissue), then anatomical context is provided, but image quality and clarity of bone structures and hardware are reduced due to noise and clutter
Solution Approach 1:
The patent applies segmentation by dividing the 3D medical image into distinct regions: a volume of interest containing bone structures and hardware, and background regions containing soft tissue and other anatomical structures. This segmentation enables selective rendering where the volume of interest is displayed with high clarity while background features are suppressed or excluded, thereby improving image quality without completely losing anatomical context.
Solution Approach 2:
The patent extracts and separates the volume of interest from the background by using segmentation algorithms to identify and isolate bone structures and hardware from soft tissue and other anatomical features. This extraction allows the system to display only the relevant content in the volume of interest while removing background clutter, directly addressing the technical contradiction between image quality and anatomical context.
2Measurement precision
If automatic segmentation is applied to reduce noise, then 3D model clarity is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by performing segmentation and thresholding operations on the 3D medical image before generating the final 3D model. By pre-processing the image to separate the volume of interest from the background and to reduce noise through thresholding, the system improves 3D model clarity while the computational burden is distributed across multiple processing stages rather than occurring all at once.
Solution Approach 2:
The patent utilizes parameter changes by adjusting threshold values and segmentation parameters to optimize the balance between noise reduction and processing time. By dynamically tuning these parameters, the system achieves clear 3D models while managing computational resources effectively, addressing the contradiction between clarity and processing time.
3Ease of operation
If multiple intensity thresholds are applied to differentiate regions, then selective display of bone and hardware is improved, but system complexity increases
Solution Approach 1:
The patent applies local quality by using different intensity thresholds for different regions of the 3D medical image. Specifically, the system uses a first intensity threshold to identify bone structures and a second intensity threshold to identify hardware within the volume of interest, while using a third threshold to suppress soft tissue in the background. This localized differentiation enables selective display capability while managing complexity through region-specific processing.
Solution Approach 2:
The patent segments the intensity range into multiple discrete thresholds, each associated with a specific anatomical structure or material type. This segmentation of the intensity domain allows the system to selectively display bone and hardware while suppressing soft tissue, providing ease of operation through automated differentiation without requiring complex manual configuration.
Data Source
AI summary
A computer-implemented method includes obtaining a three-dimensional (3D) image of a region of a body of a patient, the 3D image having feature values. The 3D image is segmented to define one or more regions of interest (ROIs). At least one region of interest (ROI) feature threshold is determined. A background feature threshold is determined. A 3D model is generated from the 3D image based on the determined at least one ROI feature threshold, the determined background feature threshold, and the segmentation. The 3D model is output for display to a user.