Intraluminal Image Processing Apparatus for Unnecessary Region Extraction
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Solution Overview
Problem
Current medical image processing techniques face challenges in accurately extracting unnecessary regions from intraluminal images, such as halation, dark part, and contents regions, which are crucial for effective medical observation and diagnosis, as these regions can obscure important features like mucous membranes and lesions.
Innovation Solution
An image processing apparatus and method that utilize multiple judging units to differentiate unnecessary regions by analyzing color information and additional feature data, such as boundary feature data for halation regions and gradient feature data for dark part regions, to precisely identify and extract these areas from intraluminal images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color information alone is used to extract unnecessary regions, then the extraction process is simple and fast, but the precision of region identification is insufficient and cannot distinguish between different types of regions
Solution Approach 1:
The patent segments the feature data into multiple types: color feature data (first feature data) and boundary feature data (second feature data). The first judging unit processes color information to identify candidate regions, while the second judging unit processes boundary information to confirm unnecessary regions. This segmentation allows the system to maintain high precision without overwhelming complexity in a single processing stage.
Solution Approach 2:
The patent performs preliminary extraction of candidate unnecessary regions using color information before final confirmation using boundary feature data. This preliminary action filters out obvious cases early in the process, reducing the computational burden on the second judging unit while ensuring high precision in the final identification.
2Measurement precision
If multiple feature data types are analyzed to precisely identify unnecessary regions, then the extraction accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary identification of candidate unnecessary regions using color information alone, which is computationally inexpensive and fast. Only the candidate regions identified in this preliminary stage are subjected to further boundary feature analysis, significantly reducing the total processing time while maintaining high accuracy for the final identification.
Solution Approach 2:
The judging process is segmented into two independent stages: first judging unit for color-based candidate identification and second judging unit for boundary-based confirmation. This segmentation allows parallel processing of different feature types and enables the system to optimize processing time by focusing computational resources only on candidate regions that require further verification.
Data Source
AI summary
An image processing apparatus includes: a first judging unit that determines an unnecessary candidate region, on a basis of first feature data based on color information of an intraluminal image; and a second judging unit that judges whether the unnecessary candidate region is an unnecessary region, based on second feature data, which is different from the first feature data, of the unnecessary candidate region.