Automated Texture Connectivity Identification in Digital Images
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Solution Overview
Problem
Current methods for identifying the connectivity of texture types in digital images, such as medical imaging data, rely heavily on manual analysis by specialists, which is time-consuming and not fully automated.
Innovation Solution
A method that partitions texture values into local neighbourhoods, determines directionality, connects nearest neighbourhoods, and establishes connectivity based on these connections to automate the identification of texture types in digital images, using techniques like k-means clustering, linear regression, and principal components analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by specialists is used to identify texture connectivity, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs automated texture connectivity analysis without requiring specialist intervention. The computer system independently partitions texture values, determines directionality, identifies nearest neighbourhoods, and establishes connectivity relationships automatically, eliminating the need for manual radiologist analysis while maintaining objective measurement precision.
Solution Approach 2:
The manual mechanical analysis process by specialists is replaced with an automated computational system. The patent substitutes human visual inspection and interpretation with algorithmic processing that partitions texture values, calculates directionality metrics, and determines connectivity relationships through computer-executable instructions, thereby increasing productivity while preserving measurement accuracy.
2Measurement precision
If manual analysis by specialists is used to identify texture connectivity, then measurement precision is improved, but loss of time worsens
Solution Approach 1:
The system performs preliminary automated processing of texture values before final connectivity determination. By pre-partitioning texture values into neighbourhoods and pre-calculating directionality metrics, the system prepares data structures that enable rapid connectivity analysis, significantly reducing the time required compared to manual specialist analysis while maintaining measurement precision.
Solution Approach 2:
The time-consuming manual analysis process is replaced with efficient computer-based algorithms. The automated system processes texture values, determines directionality, and establishes connectivity relationships much faster than human specialists can perform visual inspection, thereby reducing time loss without sacrificing measurement accuracy.
3Productivity
If automated techniques are implemented to identify texture connectivity, then productivity is improved, but device complexity worsens
Solution Approach 1:
The automated analysis system is segmented into distinct functional modules: texture value partitioning into neighbourhoods, directionality determination, nearest neighbourhood identification, and connectivity establishment. This modular segmentation manages system complexity by organizing the automated process into discrete, manageable components that can be independently implemented and validated.
Solution Approach 2:
The system manages complexity by transforming the image analysis problem into parameter space operations. By converting spatial texture information into texture value parameters, calculating directionality metrics, and representing connectivity through graph theoretical parameters, the system automates the analysis while organizing complexity through parameter transformations rather than complex spatial processing.
4Productivity
If automated techniques are implemented to identify texture connectivity, then productivity is improved, but loss of time worsens
Solution Approach 1:
The system performs preliminary organization of texture values into neighbourhoods and pre-calculates directionality metrics before final connectivity determination. This preliminary structuring of data reduces the computational burden during the actual connectivity analysis, enabling faster processing times while maintaining high productivity through efficient algorithm design.
Data Source
AI summary
A method for identifying the connectivity of texture types represented in a digital image comprising pixels, each pixel having a texture value which is representative of texture at a respective position, the method comprising: partitioning the texture values into local neighborhoods of texture values; determining a directionality for each neighborhood; using the directionality of the neighborhoods to determine their nearest neighborhood or neighborhoods; connecting the neighborhoods with their nearest neighborhood or neighborhoods; and determining the connectivity of the texture types of the digital image based on the connections formed between neighborhoods.