Medical Image Representative Frame Selection With Vessel-Aware Scoring
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Solution Overview
Problem
Existing methods for automatically analyzing medical images using machine learning models often select unsuitable frame images, leading to incorrect results due to the lack of technologies for generating representative frame images that accurately represent medical images.
Innovation Solution
A method involving a processor to calculate scores for each frame image in a medical image based on similarity and image quality, using a machine learning model to identify blood vessel regions, and generate a representative frame image by applying weight elements to merge these images, ensuring suitability for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a frame image is selected from medical images for analysis, then the analysis process can be performed, but incorrect results may occur when an unsuitable frame image is selected
Solution Approach 1:
The patent applies preliminary action by calculating scores for multiple frame images before selecting one for analysis. The scoring system evaluates image quality and vascular structure characteristics in advance, allowing the system to pre-identify suitable frame images before the actual analysis begins, thereby preventing selection of unsuitable images and improving analysis reliability
Solution Approach 2:
The patent implements feedback through a scoring mechanism that continuously evaluates frame image characteristics. The system calculates scores based on image quality metrics and vascular structure detection, providing feedback on which frame images are most suitable for analysis, and uses this feedback to guide the selection process toward accurate results
2Reliability
If multiple frame images are evaluated using complex scoring criteria, then the suitability for analysis is improved, but the processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the scoring process into distinct components: image quality scoring and vascular structure scoring. This segmentation allows independent optimization of each scoring dimension and enables parallel processing, reducing overall computation time while maintaining comprehensive evaluation of frame image suitability
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting scoring weights and thresholds based on the specific medical imaging task and image characteristics. The system can adapt the scoring parameters to prioritize certain features over others, optimizing the balance between evaluation thoroughness and processing speed for different analytical contexts
Data Source
AI summary
A method of generating a representative frame image of a medical image performed by at least one processor is disclosed, the method comprising: acquiring a medical image including blood vessels; calculating scores for each of a plurality of frame images included in the medical image; and generating a representative frame image of the medical image from the plurality of frame images based on the scores for each of the plurality of frame images.