Radiation Image Processing Device for Automatic Density Optimization
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Solution Overview
Problem
Current radiation-image processing systems require complex manual input of imaging menus and parameters for each radiation image capture, leading to inefficiencies and suboptimal image quality, especially in small hospitals without dedicated image processing personnel, and struggle to maintain consistent density and contrast across varying imaging conditions and anatomical regions.
Innovation Solution
A radiation-image processing device that calculates feature amounts based on radiation images, determines target densities for gradation processing, and performs image processing to achieve optimal image quality without the need for detailed imaging menu settings, by recognizing imaging directions and extracting anatomical regions like bone or soft tissue areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual input of imaging menus and parameters is required for each radiation image capture, then image processing can be optimized for specific imaging conditions, but operator workload increases and complexity of operation becomes very complicated
Solution Approach 1:
The image processing device automatically determines imaging conditions and performs gradation processing without requiring operator input of imaging menus. The system extracts features from the radiation image itself and autonomously selects appropriate processing parameters, making the system self-sufficient and eliminating manual intervention.
Solution Approach 2:
The patent replaces the manual mechanical operation of selecting imaging menus with an automated image processing system that uses computer algorithms to analyze radiation images and determine processing conditions. This substitution of manual operation with automated computational processing resolves the contradiction between optimization precision and operational ease.
2Adaptability or versatility
If accessory information such as imaging menu is input manually each time, then image processing can be adapted to specific imaging conditions, but work of selecting imaging menu becomes very complicated and mistyping easily occurs
Solution Approach 1:
The system automatically extracts imaging conditions from the radiation image itself without requiring manual input of accessory information. By making the system self-sufficient in determining processing parameters, it eliminates human error while maintaining adaptability to different imaging conditions.
Solution Approach 2:
The system uses feedback from the radiation image content to automatically adjust processing parameters. By analyzing the actual image data and using this feedback to determine gradation processing conditions, the system adapts to imaging conditions while eliminating manual input errors.
3Manufacturing precision
If gradation processing is performed to adjust dynamic range, then appropriate density and contrast of displayed image is achieved, but complexity of image processing increases when multiple algorithms and parameters are switched based on accessory information
Solution Approach 1:
The system automatically determines the appropriate gradation processing algorithm and parameters by analyzing the radiation image itself, eliminating the need for complex manual switching between multiple processing modes. This self-determination simplifies the processing pipeline while maintaining optimal density and contrast control.
Solution Approach 2:
The system dynamically adjusts gradation processing parameters based on automatic analysis of the radiation image content. By changing parameters automatically based on image features rather than requiring manual selection of multiple algorithms, the system maintains precise density and contrast control while reducing processing complexity.
4Ease of manufacture
If RIS is not introduced, then facility can operate without additional system requirements, but operator needs to perform input of imaging menu each time at the time of imaging
Solution Approach 1:
The image processing device operates autonomously without requiring RIS integration or manual input of imaging menus. The system extracts necessary information from the radiation image itself and performs processing automatically, maintaining simplicity of system implementation while eliminating repeated manual input requirements.
Data Source
AI summary
A feature amount calculation unit calculates, based on a radiation image acquired by irradiating a photographic subject with radiation, a feature amount of a density of the radiation image. A target density calculation unit calculates, based on the radiation image, a target density for converting the feature amount. An image processing unit performs image processing including gradation processing on the radiation image, such that the feature amount becomes the target density, to acquire a processed radiation image.