Microscope Image Color Correction via Automated Reference Localization
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Solution Overview
Problem
Existing microscopy systems face challenges in achieving accurate color correction for microscope images, particularly due to subjective manual processes and limitations in automated white balance methods, which can lead to inconsistent and user-dependent results.
Innovation Solution
A computer-implemented method and microscopy system that automatically localizes predefined object types of known colors in microscope images, allowing for the determination and application of color corrections to ensure accurate color representation without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual white balance is performed by user selection of reference areas, then color correction can be achieved, but the process is subjectively influenced and time-consuming
Solution Approach 1:
The system automatically identifies and selects reference areas (colorless regions, illumination areas, sample areas) without user intervention. The automated white balance process performs color correction independently by analyzing image statistics and determining correction factors, eliminating the need for manual user selection and significantly reducing the time required while maintaining consistent objective results.
2Productivity
If automated white balance based on global image statistics is used, then processing time is reduced, but color fidelity is compromised when only small regions are relevant
Solution Approach 1:
The image is divided into multiple distinct areas: colorless reference areas, illumination areas, and sample areas. Each area is processed separately to determine its specific color characteristics and correction needs. This segmentation allows the system to apply targeted color correction to relevant regions while ignoring irrelevant areas, maintaining both efficiency and accuracy even when only small portions of the image require correction.
Solution Approach 2:
Different color correction parameters are applied to different regions of the image based on their specific characteristics. The system determines area-specific correction factors for colorless areas, illumination areas, and sample areas, allowing each region to be optimized independently rather than applying a uniform correction to the entire image, thereby preserving local color fidelity.
3Extent of automation
If calibration patterns are used for white balance, then automated processing is possible, but the pattern must be positioned and localized manually
Solution Approach 1:
The system automatically detects and localizes calibration patterns, colorless reference areas, illumination areas, and sample areas without requiring manual user intervention. The automated detection algorithms identify these features based on their visual characteristics, enabling complete automation of the white balance process from pattern detection through color correction application, eliminating the need for manual pattern positioning and localization.
4Productivity
If pre-calibrated cameras are used, then setup time is reduced, but flexibility and accuracy across diverse samples and staining methods are limited
Solution Approach 1:
The system performs dynamic color correction by analyzing the actual color characteristics of each specific sample, illumination condition, and staining method present in the captured image. Rather than relying on fixed pre-calibrated parameters, the system adaptively determines correction factors based on real-time image statistics and area-specific analysis, enabling it to accommodate diverse samples, staining methods, and lighting conditions while maintaining speed and consistency.
Data Source
AI summary
For the color correction of microscope images, an object that corresponds to a predetermined object type of a known color is localized in a microscope image using an image processing program. Based on an image region of the localized object, a color correction is determined with which a color of the localized object in the image region is brought into accordance with the known color. The color correction is applied to at least one section of the microscope image or of another microscope image or is used in a capture of a further microscope image.