Automated Autofocusing for Biological Sample Analysis
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Solution Overview
Problem
Current automated systems for analyzing biological and chemical samples require human intervention for optical assessment, leading to subjective results and instability in disease recognition and anomaly detection.
Innovation Solution
An automated optical analysis procedure using an autofocusing system that captures and compares images of biological samples via an optoelectronic device, employing contrast and color value analysis to achieve precise focusing without human intervention, allowing for computer-assisted recognition of two and three-dimensional structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated optical analysis is implemented, then productivity and objectivity are improved, but measurement precision and reliability deteriorate due to focusing instability
Solution Approach 1:
The patent implements an autofocus mechanism that continuously monitors image sharpness and automatically adjusts the optical system to maintain optimal focus. This feedback loop ensures that automated analysis maintains measurement precision equivalent to manual expert assessment while achieving high productivity through automation.
Solution Approach 2:
The optical analysis system performs self-focusing through automated image sharpness evaluation and lens adjustment, eliminating the need for manual focusing intervention. This self-service capability enables fully automated operation while maintaining consistent focusing quality across multiple measurements.
2Ease of operation
If automated focusing is implemented, then ease of operation is improved, but device complexity increases due to additional control mechanisms
Solution Approach 1:
The patent replaces manual mechanical focusing operations with an automated optical-evaluative system that uses image processing algorithms to assess sharpness and control lens positioning. This substitution eliminates complex manual manipulation while maintaining operational simplicity through software-based control.
3Measurement precision
If manual optical assessment is performed, then measurement precision is maintained through expert judgment, but productivity decreases due to human intervention requirements
Solution Approach 1:
The system performs self-assessment of image quality through automated sharpness evaluation algorithms, replacing the need for expert human judgment. This self-service capability enables the system to rapidly evaluate multiple samples with consistent precision, achieving both high productivity and measurement accuracy without human intervention.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables routine categorization of natural and unnatural changes, including malignant conditions, with enhanced system stability and reduced need for human assessment, avoiding misfocusing and manual errors.
Implementation Method 1
capture of an image of the sample via an optoelectronic system
Implementation Method 2
capture of an image of the sample via an optoelectronic system
Implementation Method 3
employing contrast and color value analysis to achieve precise focusing
Implementation Method 4
capture of an image of the sample via an optoelectronic system
Data Source
AI summary
The invention relates to an automated method for the optical analysis of structures, in particular for the analysis and determination of biological cellular structures, and to an apparatus for this purpose, wherein an electro-optical unit generates an electronic image of two- and three-dimensional structures present in the sample, a storage medium stores the image, a computer-controlled displacement device establishes an optimized image sharpness of the image by changing the distance between sample and the optical unit, wherein the displacement device is controlled by contrast analysis and color value detection, and a computer unit compares the images generated by the electro-optical unit of two- and/or three-dimensional structures with the known structures stored in a database, and the structures registered by the optical unit are assigned by means of an algorithm to characteristic grids, structures or patterns.