Endoscope Image Selection for Blurred Blood Vessel Computation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Endoscope systems face challenges in accurately performing computations when dealing with blurred images, especially when imaging blood vessels at different depths, due to blurring caused by movement or body motion, which affects the quality of moving images and computation accuracy in existing endoscope systems.
Innovation Solution
The endoscope system employs a light source that generates sequential illumination lights, an imaging sensor to capture multi-frame image signals, and an image selection unit that chooses image signals with minimal blurring, allowing for accurate computation by selecting the image signal with the smallest blurring amount or using a blurring index value within specific ranges to generate computed image signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If computation is performed on image signals at multiple timings to extract blood vessels at different depths, then diagnostic information is enhanced, but computation accuracy deteriorates when blurred images are included
Solution Approach 1:
The patent applies preliminary action by evaluating image quality (blurring amount) before performing computation. The image quality evaluation unit assesses each image signal's blurring level, and only images meeting quality criteria are selected for subsequent blood vessel extraction computation, preventing blurred images from degrading computation accuracy
Solution Approach 2:
The patent introduces an intermediary mechanism (image quality evaluation unit and selection process) between image acquisition and computation. This intermediary evaluates image quality metrics and filters/selects appropriate images before they enter the computation pipeline, ensuring only high-quality images contribute to blood vessel extraction
2Adaptability or versatility
If imaging is performed at different timings to capture blood vessels at different depths, then diagnostic capability is improved, but image quality deteriorates due to blurring from movement and body motion
Solution Approach 1:
The system performs preliminary quality assessment of each captured image before it is used for diagnosis. By evaluating blurring amounts and other quality metrics immediately after capture, the system identifies suitable images for deep blood vessel extraction while rejecting degraded images, maintaining diagnostic reliability despite motion challenges
Solution Approach 2:
The patent implements feedback by using image quality evaluation results to control the selection and processing of image signals. The evaluation unit provides feedback about image quality to the processing system, which adjusts its operations accordingly, creating a closed-loop system that maintains image quality standards
3Loss of information
If multiple image signals are processed to calculate oxygen saturation, then diagnostic information is enhanced, but computation accuracy decreases when blurred images are used
Solution Approach 1:
Before performing oxygen saturation calculations from multiple image signals, the system preliminarily evaluates each signal's quality. This pre-assessment ensures that only images with acceptable sharpness and minimal blurring are included in the oxygen saturation computation, preventing degraded images from compromising calculation accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate computation and improved image quality even in situations with significant blurring, enhancing the precision of blood vessel imaging and oxygen saturation calculations by selecting the best image signals based on blurring criteria.
Implementation Method 1
a light source that sequentially generates first illumination light and second illumination light
Implementation Method 2
an imaging sensor that sequentially images an observation object illuminated sequentially with the first illumination light and the second illumination light
Data Source
AI summary
An image selection unit selects a B2 image signal of which an image blurring amount satisfies a first condition, from a B2 image signal at a first timing T1 or B2 image signals at the second timing T2 to an N-th timing TN. A computed image signal generation unit performs computation based on a B1 image signal at the first timing T1 and a second image signal selected in the image selection unit, thereby generating a computed image signal.