Frequency Domain Image Corruption Detection
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Solution Overview
Problem
Current high-throughput automated fluorescence microscopy systems face challenges in detecting and eliminating abnormal images from biological samples due to issues like low contrast-to-noise ratios and focus failures, which affect segmentation and tracking accuracy.
Innovation Solution
A system and method utilizing wavelet or Fourier transforms to decompose images into sub-images, calculating energy ratios across frequency channels to detect and remove corrupted images, thereby improving image quality for segmentation and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automated focus mechanisms are used during high-speed acquisition protocols, then image acquisition speed is improved, but focus accuracy deteriorates causing defocused images
Solution Approach 1:
The patent applies preliminary action by performing frequency domain analysis and corruption detection on images before they are used for segmentation and tracking. The system proactively identifies defocused or low contrast-to-noise ratio images through wavelet or Fourier transform analysis, allowing correction or rejection of corrupted images before they compromise downstream analysis accuracy.
2Reliability
If dye concentrations are reduced to avoid toxic side-effects, then cell viability is improved, but image contrast deteriorates resulting in low contrast-to-noise ratios
Solution Approach 1:
The patent replaces direct optical contrast enhancement (increasing dye concentration) with a computational approach. Instead of relying on higher dye concentrations to improve image contrast, the system uses wavelet or Fourier transform-based frequency domain analysis to detect and correct low contrast-to-noise ratio conditions, enabling the use of lower dye concentrations that maintain cell viability while preserving sufficient image quality through computational compensation.
3Measurement precision
If image processing complexity is increased to handle corruption detection, then image quality assessment is improved, but processing time increases
Solution Approach 1:
The patent extracts the essential quality assessment information by transforming images into the frequency domain using wavelet or Fourier transforms. This extraction approach isolates the critical frequency components that indicate image corruption (defocus or low contrast-to-noise ratio) without requiring complex full-image processing, enabling efficient detection of quality issues while maintaining high assessment accuracy.
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
Effectively identifies and removes defocused and low contrast-to-noise ratio images, enhancing the accuracy of cell segmentation and tracking processes, and optimizing imaging conditions for better experimental results.
Implementation Method 1
applying a wavelet transform, Fourier transform, or other frequency decomposing transform to the at least one image to decompose the at least one image into a plurality of sub-images
Implementation Method 2
applying a wavelet transform, Fourier transform, or other frequency decomposing transform to the at least one image to decompose the at least one image into a plurality of sub-images
Data Source
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AI summary
This invention, which provides a method for detecting a corruption in an image acquired from a biological sample, includes: providing at least one image of at least one cell; generating the image of the at least one cell over a period of time; determining if the at least one image of the at least one cell is corrupted; applying a wavelet transform, Fourier transform, or other frequency decomposing transform to the at least one image to decompose the at least one image into a plurality of sub-images, wherein the plurality of sub-images have a plurality of low frequency channels, a plurality of middle frequency channels and a plurality of high frequency channels; calculating a ratio based on an energy level of the plurality of low frequency channels and the plurality of middle frequency channels; and removing the at least one image of at least one cell if the at least one image is corrupted.