Biochip Sample Point Detection via Morphological Dilation
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Solution Overview
Problem
Existing biochip detection methods are labor-intensive, lack compatibility across different biochip designs, and have high computation complexity, particularly due to reliance on manual parameter input, marker design variations, and complex algorithm implementations.
Innovation Solution
A data processing method and device that involves acquiring a biochip image, performing binarization and morphological dilation operations, and connected domain detection to adaptively determine the number of rows, columns, and center positions of sample points, using OpenCV functions to simplify the process and reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter input method is used for biochip detection, then detection accuracy can be maintained, but labor cost increases and the method lacks compatibility across different biochip designs
Solution Approach 1:
The detection system automatically acquires biochip parameters directly from the image through edge detection and mesh generation algorithms, eliminating the need for manual parameter input. The system self-configures by detecting the biochip boundary, sample point distribution, and array structure automatically, enabling both high detection accuracy and universal compatibility across different biochip designs without requiring manual intervention for each chip type.
2Extent of automation
If marker-assisted method is used, then detection can be automated, but compatibility is poor due to varying marker designs from different manufacturers
Solution Approach 1:
The method extracts and removes the dependency on manufacturer-specific marker designs by directly detecting the biochip boundary and sample point array structure from the image itself. Instead of relying on external marker patterns that vary between manufacturers, the system extracts geometric features (corners, edges, mesh structure) that are universal to all biochip designs, achieving both automation and broad compatibility.
3Productivity
If existing automatic detection methods (neural network or edge detection with mesh generation) are used, then labor cost is reduced, but computation complexity is high
Solution Approach 1:
The detection process is segmented into distinct sequential steps: biochip boundary detection, sample point array identification, mesh generation, and parameter extraction. Each step processes a specific aspect of the image independently, allowing for optimized algorithms at each stage and reducing overall computational complexity compared to monolithic approaches like neural networks, while maintaining full automation and low labor requirements.
Data Source
AI summary
A data processing method for a biochip comprises: acquiring a biochip image to be detected; performing binarization processing on the biochip image to obtain a binary image; performing a morphological dilation operation on the binary image in a row direction to obtain a first image, and performing a morphological dilation operation on the binary image in a column direction to obtain a second image; performing connected domain detection on the first image in the row direction, and performing connected domain detection on the second image in the column direction, to determine the number of rows and the number of columns of a sample point array and center position information of each sample point.