Viral Particle Segmentation via Radial Density Profiles
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Solution Overview
Problem
Current image analysis methods are ineffective in objectively and reliably describing and quantifying viral particle structures, particularly intermediate and obscure forms, during virus assembly and intracellular transport, as they lack proper tools for characterization and quantification in electron microscopy images.
Innovation Solution
A method for intracellular counting and segmentation of viral particles in images, which identifies and groups round and elliptical objects by determining their radial density profiles, using techniques like Fourier transformation to differentiate viral particles and analyze their maturity stages, allowing for objective quantification and characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image analysis methods are used to analyze viral particles in electron microscopy images, then the analysis process is simple, but the measurement precision and reliability of viral particle characterization are insufficient
Solution Approach 1:
The patent segments the viral particle analysis into distinct components: radial density profile extraction, maturity stage classification, and structural parameter measurement. This segmentation enables precise characterization of different viral particle forms by analyzing specific structural features independently, thereby improving measurement precision without overwhelming complexity
Solution Approach 2:
The patent performs preliminary actions by pre-defining maturity stage categories and their corresponding radial density profile characteristics. This preliminary classification framework is established before actual image analysis, enabling systematic and reliable characterization of viral particles at different maturity stages while maintaining methodological consistency
2Reliability
If manual analysis methods are used to describe cell components, then the method is flexible, but the objectivity and repeatability are poor
Solution Approach 1:
The patent replaces manual mechanical analysis with automated image processing algorithms that objectively extract radial density profiles and classify viral particles. This substitution of mechanical/manual methods with computational automation ensures consistent, repeatable, and objective analysis across different images and observers, significantly improving reliability
Solution Approach 2:
The patent implements feedback mechanisms through automated classification algorithms that compare extracted radial density profiles against predefined maturity stage criteria. This feedback loop ensures consistent classification decisions and enables objective quantification of viral particle maturity stages, enhancing both reliability and repeatability
3Loss of information
If conventional EM techniques are used to visualize viral particles, then the structural information of mature particles is available, but intermediate and obscure particle forms cannot be properly characterized
Solution Approach 1:
The patent applies local quality analysis by focusing on specific radial density profile characteristics that are indicative of intermediate maturity stages. Rather than requiring complete structural visualization, the method identifies and analyzes local density variations that specifically characterize transitional particle forms, enabling detection of intermediate stages that conventional methods miss
Solution Approach 2:
The patent utilizes parameter changes in radial density profiles to detect and characterize intermediate viral particle forms. By monitoring changes in density distribution patterns as particles mature, the method can identify transitional states and obscure forms that do not fit standard mature particle classifications, thereby reducing information loss about particle development
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, quantification, and characterization of viral particles, including those in intermediate maturity stages, facilitating objective studies of virus assembly and the effectiveness of chemical treatments, by effectively analyzing radial density profiles and distinguishing between different viral particle forms.
Implementation Method 1
using techniques like Fourier transformation to differentiate viral particles and analyze their maturity stages
Data Source
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AI summary
The method is for intracellular counting and segmentation of viral particles in an image. An image is provided that has a plurality of items therein. A radius range of viral particles is determined. Round items in the image having a radius within the predetermined radius range are identified. Elliptical items that are formable from the predetermined radius range are determined. The round and elliptical items identified into groups are sorted. The viral particles among the round and elliptical items are identified. For example, the method may be used for intracellular counting and segmentation of siRNA treated human cytomegaloviral particles in TEM images.