Recess Boundary Detection via Radial Search Area Shifting
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Solution Overview
Problem
Existing image processing methods fail to accurately specify the boundary between a valid area and an invalid area in the peripheral edge part of a recess in images of containers with culture media due to issues like concave meniscus formation, tapered well walls, and air bubbles, leading to unclear or multiple edge features.
Innovation Solution
An image processing method that sets multiple search areas along the peripheral edge of the recess, performs edge detection, and calculates relative shift amounts to maximize pattern similarity between adjacent areas, allowing for accurate boundary specification even in unclear images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge detection methods are used to detect the boundary between valid and invalid areas, then the processing is simple and fast, but the detection accuracy deteriorates due to unclear edges caused by meniscus, tapered walls, shadows, and air bubbles
Solution Approach 1:
The image is divided into multiple search areas along the peripheral edge part of the recess, with each area processed independently to detect local edge features. This segmentation allows the system to handle unclear edges in different regions separately, improving overall boundary detection accuracy without requiring complex global processing
Solution Approach 2:
A candidate edge image is generated as an intermediary representation by synthesizing edge information from multiple search areas. This candidate edge image serves as a mediator that combines local edge features into a comprehensive boundary representation, enabling accurate boundary specification even when individual edges are unclear
2Reliability
If multiple search areas are set and comprehensive edge detection is performed to improve boundary specification accuracy, then the detection reliability improves, but the processing time and computational load increase
Solution Approach 1:
Search areas are pre-defined along the peripheral edge part before edge detection begins. By establishing the search framework in advance with predetermined positions and ranges, the system reduces computational overhead during the actual detection process while maintaining comprehensive coverage for reliable boundary specification
Solution Approach 2:
Edge detection results from multiple search areas are merged to generate a candidate edge image. By combining local edge information into a unified representation, the system achieves reliable boundary specification through integrated analysis rather than processing each area independently to completion
Data Source
Figure 1A~1B
Figure 2
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AI summary
An image processing method of the invention includes setting a plurality of search areas of a predetermined size having mutually different circumferential positions along a peripheral edge part and extending in a radial direction from a center of a recess toward the peripheral edge part near the peripheral edge part in an image (S103), executing an edge detection in each search area to obtain detected edge position and edge intensity for each search area (S104, S105), obtaining a relative shift amount for making a degree of similarity of image patterns highest for each search area when another search area adjacent to the search area in the circumferential direction is shifted in the radial direction with respect to the search area (S106, S107), and specifying a position of the boundary in one search area based on the edge positions, the edge intensities and the relative shift amounts in the search area and each of the search areas in neighboring ranges in the circumferential direction (S108).