Infrared Vessel Image Processing for Non-Invasive Glucose Testing
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Solution Overview
Problem
Existing fluorescence analysis methods for blood glucose testing inaccurately position blood vessels due to mixed spectral signals from skin and blood vessel areas, and Raman spectroscopy systems are bulky and expensive.
Innovation Solution
An image processing method using infrared light to accurately identify blood vessels by converting grayscale images into matrices, performing derivations, and using convolution kernels to select target areas, combined with fluorescence spectroscopy for non-invasive glucose testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If spectral data collection is directly performed on the image including both blood vessel area and non-blood vessel area, then the testing process is simple, but the test result accuracy deteriorates due to mixed spectral signals from skin and blood vessel areas
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) based on blood vessel detection. The processing module identifies blood vessel locations and segments the image into blood vessel areas and non-blood vessel areas, allowing separate spectral data collection from each region. This segmentation enables accurate blood glucose measurement by isolating the blood vessel signal from confounding skin signals.
Solution Approach 2:
The patent extracts the blood vessel signal from the mixed image by detecting blood vessel locations and creating masks or ROIs that isolate only the blood vessel areas. This extraction process removes the confounding skin signals while preserving the blood glucose-related spectral information, thereby improving measurement accuracy without complicating the overall testing procedure.
2Measurement precision
If Raman spectroscopy is used to measure blood glucose concentration in vivo, then the accuracy is higher than electrochemical method, but the device becomes bulky and expensive
Solution Approach 1:
The patent combines fluorescence spectroscopy with image processing techniques to achieve blood glucose measurement. By merging the fluorescence detection capability with automated blood vessel segmentation algorithms, the system achieves Raman-level accuracy using a more compact and cost-effective fluorescence-based platform, thereby reducing device complexity while maintaining high measurement precision.
Solution Approach 2:
The patent replaces the complex Raman spectroscopy system with a fluorescence spectroscopy system enhanced by image processing. This substitution uses optical fluorescence detection combined with computational image analysis to achieve comparable or superior measurement accuracy with a simpler, more compact device architecture, thereby reducing both size and cost.
3Ease of operation
If absorption spectroscopy is used for non-invasive blood glucose testing, then non-invasive testing is achieved, but spectral signals of different components are mixed together making fine separation difficult
Solution Approach 1:
The patent applies segmentation to divide the spectral data into component-specific regions. By detecting blood vessel locations in the image and creating corresponding spectral masks, the system segments the mixed absorption spectrum into blood vessel-derived signals and skin-derived signals. This enables fine separation of spectral components while maintaining non-invasive testing capability.
Solution Approach 2:
The patent extracts the blood glucose-related spectral signal from the mixed absorption spectrum by isolating the blood vessel areas through image processing. This extraction process removes confounding signals from skin and other tissues, enabling clear identification of blood glucose spectral features while preserving the non-invasive nature of the measurement.
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
Accurately recognizes blood vessel locations, improving glucose testing accuracy, enabling non-invasive, cost-effective, and real-time testing without electrochemical reactions.
Implementation Method 1
Because hemoglobin in human blood has strong absorption of infrared wavelength light waves
Implementation Method 2
a method for qualitative or quantitative analysis of fluorescence that can reflect characteristics of some substances and that is produced during a process in which the substances are in an excited state after being irradiated with ultraviolet light
Data Source
AI summary
The present invention provides an image processing method and system in analyte testing, a medium, and a device. The method includes: obtaining an infrared grayscale image of a first area; converting the image into a two-dimensional matrix, performing derivation, and recording a quantity of pixel grayscale values that remain monotonous on both sides of the minimum value point; creating an equal-size matrix of a two-dimensional matrix, and performing non-zero substitution on the minimum value point; and using a matrix as a convolution kernel to convolute the created equal-size matrix, where a probability that a position corresponding to the point is in a target area is greater as a value of an element in the obtained convoluted matrix is greater, sequencing elements and corresponding positions thereof in the convolution matrix in descending order, and selecting the target area.