Endoscopic Image Processing With Dynamic Biological Thresholds
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Solution Overview
Problem
Existing endoscope systems face issues in accurately identifying regions with specific biological information, such as low oxygen saturation, due to the use of a fixed reference value that may not adapt to changing conditions, leading to potential oversight of critical areas.
Innovation Solution
An image processing device that generates reference value color correspondence information, using a specific color to highlight regions with biological information equal to or lower than a set reference value, and adjusts the reference value dynamically based on site recognition and appropriateness determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed reference value is used for biological information display, then the display method is simple and easy to operate, but the accuracy of identifying specific biological information regions deteriorates when conditions change
Solution Approach 1:
The reference value is changed from a fixed static value to a dynamic value that automatically updates based on the average biological information of the current observation site. This allows the reference value to adapt to changing conditions (different sites, time elapsed) while maintaining ease of operation, as the system performs the update automatically without requiring manual intervention from the operator.
Solution Approach 2:
The system calculates the average biological information of the current site and uses this feedback to automatically update the reference value. This closed-loop feedback mechanism ensures that the reference value remains appropriate for the current observation conditions, improving measurement precision while keeping the operation simple through automation.
2Measurement precision
If the reference value is updated dynamically based on observation site and time, then the accuracy of identifying specific biological information regions is improved, but the device complexity increases
Solution Approach 1:
The system performs self-service by automatically calculating the average biological information of the current site and updating the reference value without requiring manual intervention. This automation of the reference value update process improves measurement precision while minimizing the increase in operational complexity, as the system manages its own parameter adjustments.
Solution Approach 2:
The reference value parameter is changed dynamically based on observed conditions (site, time) rather than remaining fixed. This parameter adaptation improves the accuracy of identifying specific biological information regions while the automatic nature of the change keeps the device complexity manageable through algorithmic rather than hardware complexity.
3Stability of the object's composition
If a fixed reference value is used, then the system is stable and simple to operate, but regions with specific biological information may be overlooked when conditions change
Solution Approach 1:
The reference value transitions from a static fixed value to a dynamic value that adapts to changing observation conditions. This dynamic adjustment maintains system stability through automatic updates while improving reliability by preventing the overlooking of regions with specific biological information that would occur with a fixed reference value.
Solution Approach 2:
The system uses feedback from continuous calculation of average biological information to adjust the reference value appropriately. This feedback mechanism ensures that the reference value remains reliable for identifying specific regions under varying conditions while maintaining operational stability through automated control.
Data Source
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AI summary
Provided are an image processing device and a method of operating the image processing device, which can prevent overlooking of a portion where biological information is specific. An image processing device (16) including an image processor, which acquires an endoscopic image, calculates biological information on the basis of the endoscopic image, sets a reference value of the biological information, generates a biological information image obtained by forming the biological information as an image so that a low value region equal to or lower than the reference value and the other region are distinguishable from each other, determines that the reference value is not appropriate in a case where a quasi-low value region in which the biological information is higher than the reference value and equal to or lower than a preset set value is present in the biological information image, and performs a notification regarding the reference value.