Flowing Liquid Sample Focusing via Object Velocity Analysis
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Solution Overview
Problem
Existing methods for focusing imaging devices on flowing liquid samples are not robust or efficient, as they do not effectively account for the movement of objects within the sample, leading to difficulties in distinguishing between focus planes containing the sample and those caused by artifacts.
Innovation Solution
A method that involves stepping through focus values while capturing frames of a flowing sample, determining focus measures and object velocities, and selecting a focus value corresponding to a local extremum with the largest object velocity to ensure focus on the sample plane, while refining the focus value through further iterations to account for varying object numbers and image contrast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional focusing methods are used on flowing liquid samples, then the focusing process can be completed, but the system cannot reliably distinguish between focus planes containing the sample and those caused by artifacts
Solution Approach 1:
The system performs preliminary actions by capturing multiple frames at different focus values before making the final focusing decision. By pre-capturing a sequence of frames across a focus search range, the system prepares the necessary data to later distinguish sample planes from artifact planes through velocity analysis, thereby improving reliability without requiring complex real-time processing.
Solution Approach 2:
The system uses feedback by calculating object velocities from captured frames and using this velocity information to guide the selection of the optimal focus value. The velocity metric provides feedback about which focus plane contains the flowing sample versus static artifacts, enabling the system to iteratively refine its focusing decision based on observed object motion patterns.
2Measurement precision
If the system captures multiple frames at different focus values to improve focusing accuracy, then the focusing precision can be improved, but the time required for the focusing process increases
Solution Approach 1:
The system performs preliminary frame capture at multiple focus values to establish velocity measurements, then uses these pre-captured frames to determine the optimal focus value. This approach allows the system to gather necessary measurement data in advance, improving precision while managing time by performing the actual capture before the decision-making process.
Solution Approach 2:
The system replaces traditional mechanical or manual focusing adjustment with an automated computational approach. By using algorithms that analyze velocity information from captured frames, the system substitutes complex mechanical focusing mechanisms with software-based focus value selection, thereby reducing the time required for the overall focusing process while maintaining high precision.
3Device complexity
If the focus measure is calculated using the entire image, then the calculation is simple, but the focus measure becomes dependent on the number of objects in each frame
Solution Approach 1:
The system applies segmentation by dividing the image into multiple patches or regions rather than treating the entire image as a single unit. By calculating focus measures on individual patches and then combining these measurements, the system reduces the influence of varying object counts across the full image while maintaining calculation feasibility. This segmentation approach isolates local focus quality from global object density variations.
Solution Approach 2:
The system implements local quality assessment by evaluating focus measures in specific local regions (patches) rather than uniformly across the entire image. This allows different regions to contribute differently to the overall focus determination, with regions containing flowing objects providing more reliable focus information. The local quality approach ensures that the focus measure reflects actual sample focus rather than being skewed by object count variations in different image areas.
Data Source
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AI summary
A method and system are provided for focusing an imaging device on a liquid sample flowing through a field of view of the imaging device. Objects are segmented in the captured frames and used to account for the fact that the sample is flowing. In some embodiments, object velocities are calculated and used in selecting an appropriate focus value. In some embodiments the calculation of a focus measure takes account of the number of objects in captured frames in order to ensure a consistent calculation of the focus measure.