Medical Imaging Screen Layout Automation via Image Similarity Matching
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Solution Overview
Problem
Medical imaging systems require complex technical definitions for user-specific arrangement and structuring of image data on screen displays, which is time-consuming and not easily manageable.
Innovation Solution
A control method that automatically structures image data for screen displays by comparing acquired data sets with pre-stored sets using similarity analysis, with options for dynamic layout adjustment and integration of DICOM attributes and image analysis for reliable matching, allowing for rapid and user-specific configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If extensive definitions are used to achieve user-specific arrangement and structuring of image data, then the layout can be customized according to user needs, but the system complexity increases and the configuration becomes time-consuming
Solution Approach 1:
The system automatically determines layout parameters by comparing acquired image data sets with pre-stored image data sets and selecting the most similar pre-stored layout. This self-service mechanism eliminates the need for users to manually configure complex technical definitions, while still achieving customized arrangements based on the specific image characteristics.
Solution Approach 2:
Multiple pre-stored image data sets with associated layout parameters are prepared in advance. When new image data is acquired, the system compares it with the pre-stored sets and automatically selects the most suitable pre-configured layout, avoiding the need for real-time complex configuration while maintaining adaptability.
2Adaptability or versatility
If manual configuration of layout parameters is performed, then user-specific customization is achieved, but the time required for configuration increases
Solution Approach 1:
The system performs automatic layout selection by comparing acquired image data with pre-stored reference data sets. The automated similarity comparison and selection process eliminates manual configuration steps, achieving customized layouts in seconds rather than requiring extensive manual setup time.
Solution Approach 2:
The manual mechanical process of configuring layout parameters is replaced by an automated computational process. The system uses data comparison algorithms and similarity metrics to automatically determine the optimal layout, substituting manual operation with automated intelligence.
3Productivity
If automatic similarity comparison is used to select layout parameters, then configuration speed increases, but the reliability of layout selection may decrease without extensive definitions
Solution Approach 1:
Manual evaluation of layout suitability is replaced by automated computational comparison using similarity metrics. The system objectively measures the match between acquired image data and pre-stored reference data, providing reliable and consistent layout selections that are both fast and accurate.
Solution Approach 2:
The system uses pre-stored image data sets with known suitable layouts as templates or copies. By comparing the acquired data with these reference copies and selecting the most similar match, the system ensures reliable layout selection based on proven configurations rather than unverified assumptions.
Data Source
AI summary
In a control method for a screen display of a medical imaging system, an image data set of a patient is acquired and a comparison of the acquired image data set is made with a number of pre-stored image data sets, each of which is stored with layout parameters for the screen display associated therewith. Display of the acquired image data set take place with the layout parameters of the pre-stored image data set that has the greatest similarity with the acquired image data set.