Endoscope Image Processing for Tool-Reflection Brightness Control
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Solution Overview
Problem
Existing endoscope systems struggle to accurately calculate detection values when a treatment tool, such as forceps, is included in the observation image, leading to halation and inadequate brightness adjustment, which darkens the region of interest.
Innovation Solution
A processing device that classifies the observation image into multiple luminance areas, determines a treatment tool area, adjusts pixel counts based on reflection rates, and calculates a detection value using weighted luminance values to optimize brightness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If the detection value is calculated from luminance values of the observation image when a treatment tool is present, then the light adjustment control reduces illumination light to minimize halation, but this darkens the region of interest and makes the observation image unsuitable
Solution Approach 1:
The patent divides the observation image into multiple luminance areas (first luminance area with high luminance values and second luminance area with low luminance values) based on luminance thresholds. This segmentation allows the system to separately process and evaluate different regions, identifying that the treatment tool occupies only the high luminance area while the region of interest remains in the low luminance area, thus preventing unnecessary global dimming.
Solution Approach 2:
The patent applies different evaluation criteria to different luminance areas. The detection value is calculated based primarily on the second luminance area (low luminance region) which contains the region of interest, rather than averaging across the entire image. This local quality approach ensures that the brightness adjustment is determined by the actual observation needs rather than being skewed by reflective artifacts from treatment tools.
2Object-affected harmful factors
If the detection value is calculated excluding areas with luminance values above a boundary luminance value, then halation is minimized, but this causes a large change in detection value around the boundary and results in discontinuous transition of average luminance value
Solution Approach 1:
The patent dynamically adjusts the weight coefficient applied to the first luminance area based on the ratio of pixels in the first luminance area to total pixels. When treatment tools are present (high ratio), the weight coefficient increases to suppress their impact. When treatment tools are absent (low ratio), the weight coefficient decreases, allowing the first luminance area to contribute normally. This dynamic adjustment ensures continuous and stable detection value transitions.
Solution Approach 2:
The patent changes the parameter of weight coefficient (multiplying factor) for the first luminance area based on the detected pixel ratio. This parameter change allows flexible control over the contribution of high luminance regions to the overall detection value, enabling the system to adapt to different surgical scenarios and maintain stable luminance transitions.
3Device complexity
If the detection value is calculated from all luminance values in the observation image, then the calculation is simple, but this includes reflected light from treatment tools and prevents adequate detection value calculation
Solution Approach 1:
The patent segments the observation image into first and second luminance areas based on luminance threshold comparisons. This segmentation enables the system to distinguish between reflective artifacts from treatment tools (first area) and actual tissue regions (second area), calculating the detection value with appropriate weighting to exclude or reduce the impact of tool reflections while maintaining calculation feasibility.
Solution Approach 2:
The patent introduces a weight coefficient as an intermediary parameter that mediates the contribution of the first luminance area to the overall detection value. This intermediary allows flexible control over the influence of high luminance regions, enabling accurate detection value calculation by suppressing tool reflections while preserving necessary image information.
Data Source
AI summary
A processing device includes: one or more processors including hardware, the one or more processors being configured to receive an observation image captured by an endoscope, calculate a representative luminance value of each of plural luminance areas resulting from classification of a specific detection area in an observation image according to luminance values, determine a treatment tool area in the observation image, count detected pixels exceeding a specific luminance value from pixels in the detection area, reduce a counted number of the detected pixels in the treatment tool area from a counted number of the detected pixels of the pixels in the detection area, calculate a weight of each of the plural luminance areas, and adjust brightness of the observation image based on the weight and the representative luminance value of each of the plural luminance areas.