Endoscope Image Analysis for Blood Flow Quantification
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Solution Overview
Problem
Existing image analysis technologies fail to accurately quantify changes in subjects over time, particularly in medical imaging, due to limitations in analyzing color component changes and luminance distribution characteristics, which are crucial for observing changes like blood flow and fat absorption.
Innovation Solution
An image analysis apparatus and system that includes an endoscope and a video processor, which time-sequentially acquire images before and after a predetermined action, extract color components, calculate distribution characteristic values, and determine the degree of change in luminance values, allowing for precise analysis of changes in medical subjects like intestinal villi.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image analysis methods are used to analyze color component changes, then the analysis process is simple, but the measurement precision of changes in blood flow and fat absorption is insufficient
Solution Approach 1:
The patent segments the image analysis process into distinct functional modules: color component extraction unit, distribution characteristic value calculation unit, and degree of change calculation unit. This segmentation allows each module to perform its specific function with high precision while maintaining overall system manageability and clarity.
Solution Approach 2:
The patent transforms the analysis from direct color component comparison to distribution characteristic value comparison. By calculating statistical parameters (mean, standard deviation, median) of color component distributions and then computing the degree of change between these parameters, the system achieves precise quantification of physiological changes such as blood flow and fat absorption.
2Measurement precision
If detailed color component analysis is performed to improve measurement precision, then the analysis accuracy increases, but the processing time increases
Solution Approach 1:
The patent extracts only the essential distribution characteristic values (mean, standard deviation, median) from the color component data, rather than performing exhaustive analysis of all pixel values. This extraction approach maintains high measurement precision for detecting physiological changes while significantly reducing processing time by focusing on key statistical parameters.
Solution Approach 2:
The patent performs partial analysis by calculating distribution characteristics for specific color components (R, G, B) and their combinations, rather than analyzing all possible color parameters. This partial action approach provides sufficient precision for medical diagnosis while avoiding excessive processing requirements.
3Measurement precision
If multiple color components are analyzed to improve analysis accuracy, then the detection capability improves, but the device complexity increases
Solution Approach 1:
The patent creates a universal analysis framework that handles multiple color components (R, G, B) and their combinations (R+G, G+B, B+R, R+G+B) through a single distribution characteristic value calculation mechanism. This multi-functional approach improves detection capability for various physiological parameters while maintaining system simplicity through standardized processing procedures.
Data Source
AI summary
An image analysis apparatus includes a region extraction unit configured to determine, as analysis target regions, respective predetermined regions in a first image and a second image that are acquired at timings before and after execution of a predetermined action to a subject and inputted through an image input unit in a state where an endoscope is continuously inserted in the subject, a distribution characteristic value calculation unit configured to obtain a first distribution characteristic value by extracting a color component of the analysis target region in the first image, and to obtain a second distribution characteristic value by extracting a color component of the analysis target region in the second image, and an image analysis unit configured to calculate the degree of change in the second distribution characteristic value with respect to the first distribution characteristic value.