Intraluminal Image Processing Apparatus for Representative Image Extraction
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Solution Overview
Problem
Current image processing techniques for extracting representative images from a series of intraluminal images struggle to efficiently reduce the burden of detailed observation and improve diagnosis by accurately identifying and prioritizing regions of interest, such as abnormal regions, within large datasets.
Innovation Solution
An image processing apparatus and method that includes a detection unit for identifying regions of interest, an image-of-interest group extracting unit for grouping identical regions, and a representative image extracting unit that prioritizes images based on correlation with the object and visibility of the regions, using color, shape, and texture features to select representative images from intraluminal images acquired by medical observation devices like endoscopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all images in the intraluminal image group are observed for accurate diagnosis, then diagnostic accuracy is improved, but the observation burden and time consumption increase significantly
Solution Approach 1:
The patent extracts only the essential information (representative images containing regions of interest) from the complete image set, rather than requiring observation of all images. The representative image extraction unit selectively outputs only those images that contain clinically significant regions, thereby reducing observation time while preserving diagnostic accuracy.
Solution Approach 2:
The patent creates simplified representations (representative images) that capture the essential diagnostic information without requiring direct observation of the entire image set. These representative images serve as efficient copies that convey the critical findings.
2Productivity
If representative images are extracted to reduce observation burden, then efficiency is improved, but the risk of missing critical abnormal regions increases
Solution Approach 1:
The patent performs preliminary detection of regions of interest using detection units that identify abnormal regions before the representative image extraction process. This preliminary action ensures that only images containing clinically significant findings are selected as representative images, guaranteeing that critical abnormal regions are not missed while improving efficiency.
Solution Approach 2:
The system uses feedback from the detection units to guide the representative image extraction process. The detection results inform which images should be selected as representative, creating a closed-loop system that ensures diagnostic reliability while maintaining efficiency.
3Speed
If simple detection methods are used to identify regions of interest, then processing speed is improved, but detection precision deteriorates
Solution Approach 1:
The patent divides the detection process into multiple specialized detection units, each responsible for detecting specific types of regions of interest using different detection methods. This segmentation allows each unit to specialize in particular detection tasks, improving overall detection precision while maintaining processing efficiency through parallel operation.
Data Source
AI summary
An image processing apparatus includes: a detection unit configured to detect images of interest including regions of interest that are estimated as an object to be detected, from a group of a series of images acquired by sequentially imaging a lumen of a living body; an image-of-interest group extracting unit configured to extract a group of images of interest including an identical region of interest, from the images of interest detected by the detection unit; and a representative image extracting unit configured to extract one or more representative images from the group of images of interest, based on at least one of correlation of the regions of interest with the object to be detected, and visibility of the regions of interest.