Intraluminal Image Region Identification via Adaptive Feature Selection
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Solution Overview
Problem
Existing image processing techniques for intraluminal images struggle to accurately identify specific regions, such as abnormalities, within the gastrointestinal tract, as they rely on overall and partial feature identification methods that are not always effective, leading to incomplete or inaccurate results.
Innovation Solution
An image processing apparatus and method that detects candidate regions for abnormalities by calculating color, shape, and texture feature data, determines the appropriate identification means based on the region type, and uses specific identifiers like color, shape, or texture feature data identifiers to accurately classify the region as vascular, neoplastic, or mucosal abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If overall identifier and partial identifier are used to identify scenes in intraluminal images, then identification coverage is improved, but identification accuracy deteriorates when the identifier cannot correctly identify a scene
Solution Approach 1:
The identification process is segmented into multiple independent identifiers (overall identifier, partial identifier, and integrated identifier). Each identifier handles specific aspects of image analysis, allowing the system to maintain comprehensive coverage while improving accuracy through specialized processing stages.
Solution Approach 2:
The system incorporates feedback mechanisms where identification results from overall and partial identifiers are fed back to the integrated identifier. This feedback loop allows the integrated identifier to correct errors and refine its judgments, thereby improving overall identification accuracy while maintaining broad coverage.
2Reliability
If multiple identification methods are applied to all regions, then identification completeness is improved, but processing time increases
Solution Approach 1:
Different identification methods are applied selectively to different regions of the image based on their characteristics. The overall identifier processes the entire image, while partial identifiers focus on specific regions of interest. This local quality approach ensures complete identification while reducing processing time by avoiding unnecessary computations in all regions.
Solution Approach 2:
The system applies partial identification actions to specific regions rather than uniformly applying all identification methods to the entire image. This selective approach maintains identification completeness for critical regions while reducing overall processing time through optimized resource allocation.
Data Source
AI summary
An image processing apparatus includes: a candidate region detection unit configured to detect, from an image acquired by imaging inside a lumen of a living body, a candidate region for a specific region that is a region where a specific part in the lumen has been captured; a candidate region information acquiring unit configured to acquire information related to the candidate region detected by the candidate region detection unit; an identification means determination unit configured to determine an identification means for identification of, based on the information related to the candidate region, whether or not the candidate region is the specific region; and an identification unit configured to identify whether or not the candidate region is the specific region by using the identification means determined by the identification means determination unit.