Cell Image Sparse Correlation Extraction for Analysis Reliability
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Solution Overview
Problem
Image processing of cell images requires significant computational resources, leading to analysis failures due to the large amount of data involved, which complicates the calculation of interactions within and among cells.
Innovation Solution
An image processing apparatus and method that employs a computing unit with a characteristic amount calculation unit, noise component elimination unit, and correlation extraction unit to reduce computational load by using sparse estimation to extract specific correlations among characteristic amounts, thereby reducing the number of correlations and analysis failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If image processing is performed on cell images to calculate interactions within and among cells, then the acquired information becomes greater in amount, but a large amount of computation is required which might result in analysis failure
Solution Approach 1:
The patent extracts only the necessary correlation information from the full set of characteristic amounts using sparse estimation. Instead of computing all possible correlations, the system selectively extracts specific correlations that are most relevant to cell interaction analysis, thereby reducing computational load while preserving essential information.
Solution Approach 2:
The patent applies partial action by computing only a subset of correlations rather than all possible correlations. The sparse estimation technique identifies and computes only the most significant correlations, performing less computation than a complete correlation analysis would require, thus avoiding analysis failures while maintaining analytical power.
2Loss of information
If all correlations among characteristic amounts are calculated, then complete interaction information is obtained, but computational load becomes excessive leading to analysis failure
Solution Approach 1:
The system extracts only the essential correlation information needed for cell interaction analysis. By using sparse estimation, it identifies and extracts specific correlations from the full set of characteristic amounts, obtaining sufficient interaction information without computing all possible correlations.
Solution Approach 2:
Instead of performing complete correlation analysis, the patent performs partial correlation analysis focused on the most relevant relationships. This partial action approach computes only the necessary correlations, significantly improving computational efficiency while maintaining the quality of interaction information.
3Productivity
If sparse estimation is used to extract specific correlations, then computational load is reduced, but the number of correlations is reduced
Solution Approach 1:
The patent extracts the most significant correlation information using sparse estimation. By applying this technique, the system identifies and extracts specific correlations that capture the essential cell interaction patterns, maintaining information quality while reducing computational burden.
Solution Approach 2:
The patent changes the parameter of correlation selection from comprehensive to selective. By using sparse estimation, it transforms the correlation analysis from computing all correlations to computing only the most significant ones, optimizing the balance between computational efficiency and information retention.
Data Source
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AI summary
An image processing apparatus includes a cell image acquisition unit configured to acquire a cell image captured with cells, a characteristic amount calculation unit configured to calculate a plurality of types of characteristic amounts on the cell image acquired by the cell image acquisition unit, and a correlation extraction unit configured to extract specific correlations from among a plurality of correlations among the characteristic amounts calculated by the characteristic amount calculation unit, based on likelihood of the characteristic amounts.