On-Demand Microscopy Intensity Correction with Embedded Model Parameters
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Solution Overview
Problem
Existing flat-field correction procedures in fluorescence microscopy are computationally intensive and require large storage for reference images, which is not suitable for speed-sensitive applications, and may lead to errors due to the large quantity of image data.
Innovation Solution
Embedding information about a reference image into the metadata field of raw images and performing flat-field correction on-demand using an intensity model developed from curve fitting, allowing for less computationally intensive processing and reducing storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If flat-field correction procedure is implemented using reference image, then image quality is improved, but computational intensity increases
Solution Approach 1:
The patent extracts only the essential correction parameters from the reference image rather than storing and processing the entire reference image. By taking out only the necessary intensity correction data, the system achieves image quality improvement while significantly reducing computational intensity and storage requirements.
Solution Approach 2:
The patent performs preliminary analysis of the reference image to pre-calculate correction parameters before actual image correction is needed. This preliminary action prepares the correction data in advance, reducing the computational burden during real-time or on-demand image correction while maintaining quality improvement.
2Manufacturing precision
If reference image is stored for offline correction, then image quality is improved, but storage requirements increase
Solution Approach 1:
The patent extracts only the essential correction parameters from the reference image rather than storing the entire reference image. This extraction approach reduces storage requirements from storing complete high-resolution images to storing compact parameter sets while maintaining the ability to improve image quality through flat-field correction.
Solution Approach 2:
The patent creates a simplified copy or representation of the reference image in the form of correction parameters or metadata. Instead of storing the full reference image, the system stores a condensed version containing only the necessary correction information, reducing storage requirements while preserving correction capability.
3Manufacturing precision
If flat-field correction is performed on all images, then image quality is improved, but processing time increases
Solution Approach 1:
The patent applies partial correction by performing flat-field correction only on regions of the image where intensity roll-off is significant, rather than processing the entire image. This partial action approach maintains image quality improvement in critical areas while reducing overall processing time and computational resources required.
Solution Approach 2:
The patent performs preliminary identification of regions requiring correction before applying the flat-field correction process. By pre-processing to identify only the necessary areas, the system reduces processing time while maintaining quality improvement where it matters most.
Data Source
AI summary
Methods and systems are provided for correcting intensity variation in a raw image acquired via a microscopy imaging system. In one example, a method includes acquiring a reference image and developing an intensity model based on the reference image wherein the intensity model comprises a pre-determined number of a plurality of model parameters obtained via curve fitting a single analytical function or a linear combination of a plurality of analytical functions, embedding model parameters of the intensity model of the reference image into the raw image for image processing at a later time; and correcting the raw image based on the intensity model of the reference image on-demand. As another example, a method includes utilizing the plurality of parameters of the intensity model as a hardware signature to indicate whether a plurality of optical system parameters is maintained.