Microscope Movement Detection via Pixel Intensity Analysis
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Solution Overview
Problem
Current methods for detecting movements of a sample with respect to a microscope's objective often require manual selection of reference objects and lack automation, which can lead to incomplete compensation of sample movements, affecting the effective spatial resolution in microscopic imaging.
Innovation Solution
A method and device that automatically detect movements by imaging the sample onto an image sensor, analyzing variations in light intensities, and selecting a subset of pixels to compare with reference images, allowing for the detection and compensation of sample movements without manual object image selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual selection of reference objects is used for movement detection, then the detection process can be performed, but the automation level is low and the process is time-consuming
Solution Approach 1:
The system automatically performs reference object selection, image comparison, and movement detection without requiring manual intervention. The evaluation device autonomously identifies reference objects in the sample image, compares them with reference images, and detects sample movements, enabling the system to serve itself rather than requiring operator assistance.
Solution Approach 2:
The system changes the parameter of automation from manual to automatic by implementing an automated workflow that includes automatic reference object identification, automatic image comparison, and automatic movement detection. This parameter change eliminates the need for manual selection while maintaining detection accuracy.
2Measurement precision
If complete compensation of sample movements is pursued, then spatial resolution is improved, but the device complexity increases
Solution Approach 1:
The patent replaces complex mechanical movement compensation systems with an optical-based detection and computational correction approach. By using optical imaging to detect movements and computational methods to compensate, the system achieves high spatial resolution without requiring complex mechanical stabilization mechanisms.
Solution Approach 2:
The evaluation device acts as an intermediary between the sample and the imaging system, detecting movements through reference objects and providing correction information. This intermediary approach enables movement compensation without directly modifying the mechanical imaging system, thereby reducing overall device complexity.
3Measurement precision
If reference objects with specific characteristics are used, then movement detection accuracy is improved, but the ease of operation decreases due to manual selection requirements
Solution Approach 1:
The system automatically identifies and selects suitable reference objects from the sample image based on their optical characteristics, eliminating the need for manual selection. The evaluation device autonomously performs reference object identification, comparison, and movement detection, making the operation simple while maintaining high detection accuracy.
4Productivity
If automated pixel selection based on intensity variations is implemented, then detection speed is improved, but the computational complexity increases
Solution Approach 1:
The system segments the image analysis process by first selecting a subset of pixels based on intensity variations, then performing movement detection only on these selected pixels. This segmentation approach speeds up detection by avoiding processing of all pixels while the computational complexity is managed through efficient algorithms for pixel selection and comparison.
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
Enables the automatic detection and compensation of sample movements, improving the effective spatial resolution in microscopic imaging by selecting suitable pixels based on intensity variations and comparing them to reference images, thereby maintaining the sample's stability relative to the objective.
Implementation Method 1
imaging the sample onto an image sensor which comprises an array of pixels by means of the objective
Implementation Method 2
light coming from the sample is registered at the pixels of the image sensor
Data Source
AI summary
For detecting movements of a sample with respect to an objective, the sample is imaged onto an image sensor comprising an array of pixels by means of the objective. Images of the sample are recorded in that light coming from the sample is registered at the pixels. Variations of intensities of the light coming from the sample and registered at the pixels are determined during a set-up period in that a temporal course of the intensity of the light, which has been registered at a respective one of the pixels over the set-up period, is analyzed. Using these variations as a criterion, a subset of not more than 90% of the pixels of the image sensor is selected. Parts of the images that each correspond to the selected subset are compared to parts of at least one reference image that also correspond to the subset.