Image Quality Assessment for Scanning Imaging Systems
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Solution Overview
Problem
Scanning imaging systems are often assessed subjectively, making it difficult to accurately gauge their performance and detect gradual degradation due to budgetary constraints and time pressures.
Innovation Solution
A method and apparatus for assessing image quality using a computer program that calculates sharpness, contrast, and noise-independent indicators, determining a quality score by processing image data from scanning imaging systems, such as scanning laser ophthalmoscopes, to provide an objective and reliable evaluation of image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective visual inspection by operators is used to assess image quality, then the assessment process is simple and quick, but the measurement precision and reliability of performance evaluation deteriorates
Solution Approach 1:
The patent replaces the mechanical/visual inspection process with an automated computational system that calculates objective image quality metrics (sharpness, contrast, noise) through mathematical processing of image data, eliminating subjective human evaluation while maintaining operational simplicity
Solution Approach 2:
The system enables self-assessment of image quality by automatically processing images and generating quality scores without requiring operator intervention or subjective judgment, allowing the imaging system to evaluate its own performance objectively
2Loss of time
If subjective visual inspection is used for image quality assessment, then time consumption is reduced, but the ability to detect gradual performance degradation deteriorates
Solution Approach 1:
The system provides continuous automated feedback by calculating objective quality metrics and comparing them against reference values or historical data, enabling reliable detection of gradual performance degradation over time through systematic monitoring rather than intermittent visual checks
Solution Approach 2:
The system performs preliminary automated analysis of image quality metrics before clinical use, establishing baseline performance and enabling early detection of degradation trends, allowing preventive maintenance before significant performance loss occurs
3Measurement precision
If automated objective assessment is implemented, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent divides the image quality assessment into separate modular components: sharpness calculation module, contrast calculation module, noise calculation module, and integration module. Each module performs a specific function independently, making the complex system manageable and maintainable while achieving high measurement precision
Solution Approach 2:
The system uses universal image processing algorithms and mathematical models that can assess multiple quality parameters (sharpness, contrast, noise) simultaneously from the same image data, reducing overall system complexity by avoiding separate dedicated systems for each metric
4Measurement precision
If noise-independent sharpness and contrast measures are calculated, then image quality assessment accuracy improves, but computational complexity increases
Solution Approach 1:
The patent transforms the image data into different parameter domains (e.g., frequency domain, gradient domain) to calculate sharpness and contrast metrics that are inherently more resistant to noise, achieving accurate measurements through mathematical transformation rather than complex noise filtering algorithms
Data Source
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AI summary
A method, apparatus, and computer-readable medium, for assessing image quality of an image produced by a scanning imaging system. The method comprises acquiring (S10) image data of an image produced by the scanning imaging system and calculating (S20 to S40), for each section of the image: a respective first value measuring at least one of sharpness or contrast of at least a part of the section, the measuring depending on noise, a respective second value measuring noise in at least a part of the section, and a respective third value indicating image quality, by combining the first and second values. The combining is such that calculated third values have a weaker dependency on the noise than the first values. The method further comprises determining (S50) a quality score that is indicative of image quality of the image based on a variation of the calculated third values among the sections.