Serum Agglutination Imaging With Multi-Feature Well Classification
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Solution Overview
Problem
Existing serum-agglutination tests for infectious diseases are often performed manually, requiring visual interpretation and are prone to operator-dependent errors, with automated systems lacking reliability in feature extraction beyond simple positive/negative discrimination.
Innovation Solution
An apparatus with an image detector and processing unit that analyzes microplate images to extract features like pixel aggregates, closed contours, and circular/elliptical elements, classifying samples based on these features for reliable and automated serum-agglutination testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual interpretation is used for serum-agglutination tests, then operator flexibility is maintained, but reliability and objectivity of results deteriorate due to operator-dependent errors
Solution Approach 1:
The patent replaces the manual visual interpretation mechanism with an automated image analysis system using a camera and processing unit. The system captures images of the microplate and automatically extracts features such as pixel aggregates, closed contours, and circular/elliptical elements to determine agglutination results, eliminating operator-dependent errors while maintaining test reliability.
Solution Approach 2:
The patent creates a digital copy of the visual test results through image capture. Instead of direct human observation, the system captures an optical copy (image) of the microplate contents and processes this copy through algorithmic analysis, enabling automated interpretation while preserving the original test visualization for verification.
2Productivity
If automated image analysis is implemented, then productivity and throughput are improved, but measurement precision deteriorates due to difficulty in extracting meaningful features beyond simple positive/negative discrimination
Solution Approach 1:
The patent segments the image analysis into multiple distinct feature extraction components: pixel aggregate detection, closed contour identification, and circular/elliptical element recognition. Each segment focuses on a specific aspect of agglutination morphology, allowing the system to capture comprehensive test information while maintaining high processing speed and productivity.
Solution Approach 2:
The patent transforms the visual test data into multiple quantitative parameters including number of pixel aggregates, area of aggregates, number of closed contours, and circularity metrics. This parameter transformation enables precise measurement of agglutination characteristics, improving measurement precision while maintaining automated high-throughput processing capability.
3Measurement precision
If multiple feature extraction methods are combined, then measurement precision is improved through deterministic metric features, but device complexity increases
Solution Approach 1:
The patent merges multiple feature extraction methods (pixel aggregate analysis, contour detection, and shape recognition) into a unified processing unit. This consolidation allows the system to leverage the strengths of each method for precise sample classification while managing device complexity through integrated architecture rather than separate independent systems.
Data Source
AI summary
An apparatus having a housing area for housing a plate with a plurality of wells containing a sample to be analyzed, a processing unit, and an image detector for acquiring images of the plate located in the housing area is disclosed. The processing unit controls the acquisition of an image containing a well, extracting a plurality of features from the image including a number of aggregates of pixels detectable in the image, a number of closed contours detectable in the image, and a number of elements having a determined partially circular and/or elliptical geometry detectable in the image, classifying the sample contained in the well, and providing information on the positivity of the sample through the classification.