Ultrasonic Imaging Gray Level Optimization via Dynamic Sub-Area Analysis
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Solution Overview
Problem
Conventional ultrasonic imaging systems face inefficiencies in adjusting Time Gain Compensation (TGC) and dynamic range due to assumptions of uniform gray-level changes, leading to inaccuracies in image analysis, especially when dealing with heterogeneous tissue areas and noise.
Innovation Solution
The system dynamically searches non-evenly divided sub-areas within ultrasonic images to analyze gray-level changes in depth, calculating optimized parameters for uniform brightness and suppressing noise, thereby improving image equilibrium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional methods assume uniform gray-level changes and divide images evenly, then the analysis process is simplified, but the accuracy of image analysis deteriorates due to heterogeneous tissue areas and noise
Solution Approach 1:
The patent divides the ultrasonic image into multiple sub-areas along the depth direction, but unlike conventional even division, it dynamically adjusts the division based on detected tissue boundaries and noise regions. This segmentation allows different tissue types to be analyzed separately, improving accuracy while maintaining manageable complexity.
Solution Approach 2:
The patent applies different analysis methods to different sub-areas based on their local characteristics. Homogeneous tissue areas undergo standard gray-level analysis, while heterogeneous areas with noise or structural variations receive specialized processing, ensuring each region is analyzed with appropriate precision.
2Measurement precision
If manual adjustment of TGC and dynamic range is performed to optimize image quality, then image quality improves, but the diagnosis time increases
Solution Approach 1:
The system automatically performs gray-level optimization by detecting tissue boundaries, analyzing gray-level changes in each sub-area, and calculating optimal TGC and dynamic range parameters without operator intervention. This self-service capability eliminates manual adjustment time while maintaining image quality.
Solution Approach 2:
The patent performs preliminary analysis of the ultrasonic image to detect tissue boundaries and classify areas before optimizing parameters. This preliminary action enables the system to pre-determine the optimal analysis approach for each region, streamlining the overall process.
3Device complexity
If average gray level of evenly divided areas is used for analysis, then the calculation is simplified, but the result accuracy deteriorates due to inclusion of noise and structural tissue areas
Solution Approach 1:
The patent extracts and identifies noise sub-areas and structural tissue areas from the image, then excludes them from the gray-level averaging calculation. Only homogeneous tissue sub-areas are included in the final gray-level analysis, significantly improving accuracy.
Solution Approach 2:
The patent dynamically adjusts the composition of sub-areas included in the gray-level analysis based on real-time detection of tissue homogeneity. Rather than using fixed even divisions, the system adapts the analysis regions to match actual tissue boundaries and characteristics.
Data Source
AI summary
The invention relates to an ultrasonic system and a method for optimizing gray level value of ultrasonic images used in the ultrasonic system, the method comprises: an acquiring step for acquiring the ultrasonic image; a searching step for searching non-evenly divided sub-areas from the acquired ultrasonic image; analyzing step for analyzing the change of gray level in each of the sub-areas in the direction of depth; and calculating step for calculating an optimized gray level value of the ultrasonic image based on the result of the analyzing step. By implementing the method of present invention, a better image equilibrium effect is obtained in the ultrasonic system.