Image Segmentation for Medical Imaging Artifact Reduction
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Solution Overview
Problem
Medical imaging techniques often produce images with artifacts due to attenuation objects like calcifications and metal implants, reducing image accuracy and diagnosis quality.
Innovation Solution
A method for image segmentation that determines an initial boundary of a target object within image data, generates a closed boundary, and segments the target portion using a combination of initial and closed boundaries, improving the accuracy of image segmentation and correcting image data to reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image reconstruction methods are used, then the imaging process is simple and fast, but artifacts are generated reducing image accuracy
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct stages: initial boundary detection, closed boundary generation, and target portion segmentation. This multi-stage segmentation approach enables precise isolation of attenuation objects from the subject, improving image accuracy by removing artifacts while managing complexity through structured processing steps.
2Measurement precision
If artifact reduction through segmentation is implemented, then image quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing initial boundary detection and closed boundary generation before final segmentation. This preparatory processing establishes accurate boundaries in advance, enabling the final segmentation step to proceed efficiently with reduced computational burden, thus balancing image quality improvement with processing time management.
3Manufacturing precision
If precise boundary detection is performed, then segmentation accuracy improves, but the complexity of boundary processing increases
Solution Approach 1:
The patent segments boundary processing into two distinct phases: initial boundary detection and closed boundary generation. This segmentation of the boundary processing task enables precise target identification while managing complexity by handling different aspects of boundary detection in separate, specialized steps rather than attempting to solve all boundary issues simultaneously.
Data Source
AI summary
The present disclosure provides systems and methods for image segmentation. The method may include determining, based on image data of a subject, an initial boundary of a target object inside the subject, wherein a difference between a first parameter value of at least one parameter of the target object and a second parameter value of the at least one parameter of the subject satisfies a condition; determining, based on the initial boundary of the target object, a closed boundary of the target object; and segmenting, based on the initial boundary and the closed boundary, a target portion corresponding to the target object from the image data.