Automated Structure Identification Through Plasmonic Color Contrast
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Solution Overview
Problem
Conventional optical microscopy techniques primarily rely on intensity contrast using stains, which limits the ability to distinguish different structures within samples, especially in optically transparent specimens, and lack the capability for automated image analysis.
Innovation Solution
Utilizing a sample holder with a plasmonic layer featuring a periodic array of sub-micron structures to enhance image contrast through color differentiation, enabling automated image processing and recognition of structures based on localized refractive indices, employing methods such as color filtering, feature extraction, and image recognition systems like artificial neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optical microscopy with intensity contrast staining is used, then the method is simple and well-established, but the ability to distinguish different structures within samples is limited
Solution Approach 1:
The patent employs color contrast microscopy where different structures in the sample exhibit distinct colors due to their optical properties interacting with the plasmonic layer. This allows automated differentiation of structures based on color information rather than requiring complex staining procedures, thereby improving measurement precision for structure identification while maintaining relatively simple device complexity
Solution Approach 2:
The patent introduces an intermediary processing layer that includes color filtering, feature extraction, and image recognition systems. This intermediary layer processes the color-rich images to automatically identify and differentiate structures, bridging the gap between the optical microscopy system and the analysis requirement without significantly increasing overall system complexity
2Loss of information
If stained samples are used in conventional microscopy, then intensity contrast is achieved, but only single colour information is available which limits structure distinction
Solution Approach 1:
The patent utilizes the natural color variations that arise when light interacts with different structures through the plasmonic layer, eliminating the need for artificial staining. This preserves full color information (multiple wavelengths) for differentiation while simplifying sample preparation, as no staining reagents or complex preparation protocols are required
Solution Approach 2:
The sample structures themselves generate the color contrast information through their intrinsic optical properties when illuminated through the plasmonic layer. The system leverages the sample's own characteristics rather than requiring external staining agents, thereby preserving color information richness while reducing sample preparation complexity
3Productivity
If automated image analysis is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The automated analysis system is segmented into distinct functional modules: color filtering module, feature extraction module, and image recognition module. This segmentation allows each component to perform a specific function efficiently, increasing overall productivity while managing device complexity through modular design where each module can be independently optimized and maintained
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 accurate identification and differentiation of structures within samples, particularly cells, by leveraging color contrast, improving the ability to distinguish between healthy and abnormal cells, including cancerous cells, and providing automated analysis of biological samples.
Implementation Method 1
through use of a sample holder having a plasmonic layer including a periodic array of sub-micron structures... through interaction of the light with the sample and the plasmonic layer, a colour contrast is exhibited in the receive image
Implementation Method 2
a sample holder having a plasmonic layer including a periodic array of sub-micron structures... areas of the sample having different dielectric constant appear in the image with different colours
Data Source
AI summary
An automated method of identifying a structure in a sample is disclosed. The method includes receiving at least one digital image of a sample wherein at least one localized structural property of the sample is visible in the image based on the color of received light. The method involves processing the at least one image, based on the received color information to selectively identify said structure. The method can include color and/or morphology based image analysis.