Capsule Endoscope Bubble Detection via Edge Intensity
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Solution Overview
Problem
Current image processing methods for intra-cavity images captured by capsule endoscopes face accuracy issues in organ determination and diseased site detection due to the presence of air bubbles, as they interfere with color level calculations and overlap with mucous membrane and diseased site distributions in feature space.
Innovation Solution
An image processing apparatus and method that calculates edge intensity and correlation values between pixel data and a pre-defined bubble model to detect bubble areas, utilizing edge intensity calculation methods like quadratic differentiation and correlation value calculation techniques such as Fourier transforms, allowing for accurate bubble area detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If color level averaging is performed for organ determination, then organ classification can be performed, but accuracy deteriorates due to bubble interference
Solution Approach 1:
The patent applies preliminary action by detecting and removing bubble pixels from the image before performing color level averaging for organ determination. The edge intensity calculation and bubble model correlation are performed in advance to identify and eliminate bubble interference, ensuring that subsequent color-based organ classification is not affected by bubbles.
2Ease of operation
If clustering is performed in feature space for diseased site detection, then detection capability is provided, but accuracy deteriorates due to bubble distribution overlap
Solution Approach 1:
The patent applies the taking out principle by extracting and removing bubble pixels from the image before performing clustering analysis. By using edge intensity calculation and bubble model correlation to identify bubble regions, the method extracts only the relevant mucous membrane pixels for clustering, eliminating the harmful overlap between bubble and diseased site distributions in feature space.
3Device complexity
If no bubble detection is performed, then processing is simpler, but organ determination and diseased site detection accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary bubble detection mechanism that uses edge intensity calculation and correlation with a predefined bubble model. This intermediary step identifies bubble pixels through their characteristic edge patterns, serving as a mediator between the raw image and the subsequent organ determination or diseased site detection processes, thereby improving accuracy without excessive complexity.
Data Source
AI summary
An image processing apparatus includes an edge intensity calculator which calculates an edge intensity of a pixel in an image; and a correlation value calculator which calculates a correlation value between the calculated edge intensity and a bubble model set in advance based on characteristics of a bubble image. The apparatus also includes a bubble area detector which detects a bubble area based on the calculated correlation value.