Endoscopic Image Quality Assessment via Effective Region Analysis
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Solution Overview
Problem
Existing image quality assessment techniques for endoscopic images are inadequate as they focus on peak signal-to-noise ratio, edge structure, and sharpness, failing to effectively evaluate the quality of endoscopic images, which is crucial for determining the cleanliness of organs before surgery and assessing the standardization of medical procedures.
Innovation Solution
A method and system for image quality assessment that determine the quality of endoscopic images by analyzing a first image based on effective regions and similarity parameters between frames, using machine learning models for segmentation and similarity analysis to assess the proportion of effective regions and structural similarity between frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing image quality assessment techniques (focusing on peak signal-to-noise ratio, edge structure, and sharpness) are used, then general image quality can be evaluated, but they are not suitable for endoscopic image quality assessment
Solution Approach 1:
The patent changes the assessment parameters from traditional general image quality metrics (peak signal-to-noise ratio, edge structure, sharpness) to endoscopic-specific parameters including effective region proportion, similarity with reference images, and cleanliness evaluation metrics. This parameter transformation enables accurate assessment of endoscopic images while maintaining adaptability to the specific requirements of medical endoscopy.
2Measurement precision
If manual assessment of endoscopic image quality is performed, then accurate evaluation of cleanliness and surgical standards can be achieved, but it consumes significant time and resources
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated computer-based system that uses image processing algorithms, effective region detection, and similarity comparison with reference images. This substitution maintains high assessment accuracy while dramatically improving efficiency by eliminating manual time consumption and human resource requirements.
Solution Approach 2:
The system enables self-service assessment where the computer automatically evaluates endoscopic images without requiring manual intervention. The system performs self-assessment of image quality, cleanliness standards, and surgical procedure evaluation, making the process autonomous and highly efficient while maintaining precision through algorithmic analysis.
3Measurement precision
If comprehensive analysis of multiple video frames is performed, then more accurate image quality assessment can be achieved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential features from multiple video frames for assessment, specifically focusing on effective regions and similarity with reference images. By extracting only the critical information needed for endoscopic quality assessment rather than analyzing all frame details, the system achieves high accuracy while reducing computational complexity and processing requirements.
Data Source
AI summary
Embodiments of the present disclosure provides a method and a system for image quality assessment. The method may include obtaining a first image of a target video, wherein the first image may be determined based on a target video frame in the target video; and determining a target image quality of the target video frame based on at least one of a first parameter and a second parameter of the first image, wherein, the first parameter may be determined based on an effective region in the first image, the second parameter may be determined based on a first similarity parameter between the first image and a second image, and the second image may be determined based on a video frame other than the target video frame in the target video.