Endoscope Image Analysis Brightness Correction
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Solution Overview
Problem
Existing image analysis techniques fail to accurately detect changes in subjects from time-sequentially acquired images, particularly in endoscopic observations, due to brightness distribution issues caused by illumination and optical factors, which affect the reliability of color component analysis.
Innovation Solution
An image processing apparatus and method that generates brightness distribution correction data for each image frame, using closed curve-extracted areas to correct for brightness slope, allowing for accurate analysis of changes between frames by isolating the impact of illumination and optical influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brightness distribution correction is not applied to endoscopic images, then the image processing is simpler and faster, but the accuracy of color component analysis and change detection deteriorates due to illumination and optical factors
Solution Approach 1:
The patent applies brightness distribution correction in advance before color component analysis and change detection. By pre-processing the endoscopic images to correct illumination and optical factors, the system ensures accurate color analysis without adding complexity to the subsequent analysis steps. The correction data is generated and applied beforehand, allowing simpler downstream processing.
Solution Approach 2:
The patent introduces brightness distribution correction data as an intermediary element between the raw endoscopic image and the color component analysis. This correction data acts as a mediator that compensates for illumination and optical factors, enabling accurate color analysis without requiring complex integrated processing. The correction data can be generated separately and applied to multiple images.
2Measurement precision
If brightness distribution correction data is generated for each image frame, then the accuracy of change detection between frames is improved, but the processing time and computational load increase
Solution Approach 1:
The patent generates brightness distribution correction data specifically for regions of interest identified by closed curves in each image frame, rather than processing the entire image. This localized approach maintains accuracy for the areas being analyzed while reducing the overall computational load and processing time. The correction is applied selectively to relevant regions.
Solution Approach 2:
The patent applies brightness distribution correction selectively to specific areas within each image frame that contain the subject of interest, rather than correcting the entire image. This partial action approach maintains sufficient accuracy for change detection while minimizing processing time and computational resources by focusing only on relevant regions.
3Measurement precision
If closed curve extraction is used to identify areas for correction, then the precision of brightness correction in relevant regions is improved, but the complexity of image processing increases
Solution Approach 1:
The patent segments the endoscopic image by extracting closed curves that define regions of interest containing the subject. This segmentation allows brightness correction to be applied precisely to the relevant areas while ignoring background regions. The closed curve extraction divides the image into corrected and uncorrected portions, improving precision without requiring complex correction algorithms across the entire image.
Data Source
AI summary
A video processor receives first and second images respectively acquired by an endoscope at first and second timings before and after a predetermined function is applied to a subject, generates first and second correction images respectively using first and second images, generates first and second post-correction images respectively obtained by causing the first and second correction images to act on the first and second images, extracts color components in the second post-correction image to find first and second distribution characteristic values, and calculates a degree of change of the second distribution characteristic value from the first distribution characteristic value.