Automated X-ray Image Verification Using Segmentation and Reference Comparison
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Solution Overview
Problem
Current X-ray image quality checking methods are time-consuming, labor-intensive, and prone to errors, often requiring manual inspection by X-ray assistants, leading to unnecessary patient re-exposure and additional imaging time.
Innovation Solution
An automated method for checking X-ray image data using machine-learning algorithms for segmentation and comparison with reference data, determining the suitability of images for diagnosis and reducing the need for manual inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual image review by X-ray technician is performed, then image quality assessment can be conducted, but additional time and personnel costs are required
Solution Approach 1:
The patent replaces the manual mechanical review process by X-ray technicians with an automated computer-based image analysis system. The system uses algorithmic processing to automatically assess image quality, segment anatomical structures, and determine diagnostic suitability, eliminating the need for manual mechanical inspection while maintaining or improving assessment accuracy.
Solution Approach 2:
The image quality assessment system performs self-evaluation through automated algorithms that independently analyze image data without human intervention. The system segments images, identifies anatomical structures, compares them against reference data, and automatically determines diagnostic quality, enabling the system to serve itself rather than requiring external technician review.
2Reliability
If repeat X-ray exposure is performed to ensure image quality, then diagnostic accuracy is improved, but additional radiation dose to patient is incurred
Solution Approach 1:
The system performs preliminary automated quality assessment of X-ray images immediately after acquisition but before clinical use. By pre-evaluating image quality through automated segmentation and analysis, the system identifies suitable images in advance, preventing the need for repeat exposures and thereby avoiding additional radiation doses to patients.
Solution Approach 2:
The system provides immediate feedback on image quality through automated analysis, informing whether an image is suitable for diagnosis. This feedback mechanism allows clinicians to make informed decisions about image acceptance or rejection without requiring repeat exposures, thus preventing unnecessary radiation exposure while ensuring diagnostic accuracy.
3Productivity
If automated image analysis is implemented, then processing speed is improved, but system complexity increases
Solution Approach 1:
The automated analysis system segments the complex task of image quality assessment into distinct modular components: image segmentation, anatomical structure detection, reference data comparison, and quality determination. Each module handles a specific aspect of the analysis independently, making the overall complex system more manageable and easier to implement while maintaining high processing speed.
Data Source
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AI summary
A method for the automated review of radiographic image data (RI) from a patient's examination area is described. Within this method, radiographic image data (RI) from the examination area is received. Furthermore, the radiographic image data (RI) is segmented, and anatomical structures within the individual segments are detected. Additionally, the closest reference image data (RI) to the segmented radiographic image data (SRI) is determined. This process is based on a comparison of the segmented radiographic image data (SRI) with reference image data (RI) from a reference database (DB), where the reference image data (RI) in the reference database (DB) each contains quality information regarding the image quality of the reference image data (RI). Finally, based on the quality information of the determined reference image data (RI), a decision is made as to whether the received radiographic image data (RI) is retained or discarded.An imaging procedure is also described. Furthermore, an image data verification device (20) is described. Additionally, an X-ray imaging device (30) is described.