Intraluminal Image Processing for Mucosal Region Discrimination
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Solution Overview
Problem
Current image processing techniques for intraluminal images captured by endoscopes face challenges in accurately distinguishing between mucosal and non-mucosal regions, particularly in identifying residue regions that are not relevant for diagnosis, which hinders efficient medical image analysis.
Innovation Solution
An image processing device and method that detects candidate regions in intraluminal images using pixel feature data and structure edge information, employing a candidate region detecting unit, structure edge region detecting unit, and region discriminating unit to differentiate between mucosal and non-mucosal regions based on relative positional relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing uses only color tone feature data to detect mucosal regions, then the processing is simple and fast, but the precision of distinguishing mucosal from non-mucosal regions deteriorates
Solution Approach 1:
The image processing is divided into multiple independent modules: a candidate region detecting unit that identifies potential mucosal regions using color tone features, and a region discriminating unit that determines whether these candidates are truly mucosal or non-mucosal (residue) regions. This segmentation allows each module to specialize in one aspect of the analysis.
Solution Approach 2:
Structure edge information serves as an intermediary element between the candidate region detection and final discrimination. The structure edge detecting unit extracts edge information from the image, and this edge data is used by the region discriminating unit to resolve ambiguities that color tone alone cannot distinguish, particularly between mucosal and residue regions.
2Productivity
If all intraluminal images are manually observed and diagnosed, then diagnostic accuracy is maintained, but the time consumption and workload increase significantly
Solution Approach 1:
The system performs preliminary automated analysis by detecting candidate mucosal regions and discriminating them from non-mucosal regions before final medical diagnosis. This preliminary action filters out obvious non-mucosal areas (residues) and prepares structured information for the physician, reducing the burden of manual review while maintaining diagnostic quality.
Solution Approach 2:
The region discriminating unit provides feedback to the diagnostic process by identifying and flagging regions that are likely to be residues versus those that are likely to be mucosal. This feedback mechanism allows the system to guide physician attention to areas requiring detailed review while automatically handling routine discrimination tasks.
Data Source
AI summary
In an image processing device that discriminates between a mucosal region and a non-mucosal region that are contained in an intraluminal image, the image processing device includes a residue candidate region detecting unit that detects a residue candidate region to be discriminated whether the candidate region is a mucosal region or not, on the basis of a feature data of each pixel that constitutes the intraluminal image, a structure edge region detecting unit that detects a structure edge contained in the intraluminal image, and an approximate structure edge line calculating unit and an overlap deciding unit that discriminate whether the residue candidate region is a mucosal region or not, based on a relative positional relationship between the structure edge and the residue candidate region.