Volume Rendering Cross-Section Correlation Analysis
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Solution Overview
Problem
Texture-based volume rendering methods face high processing loads and reduced display accuracy when the number of cross-sections is low, especially when volume data is rotated, due to large differences in cross-section content.
Innovation Solution
The method involves extracting regions from each cross-section based on brightness values, deleting cross-sections with high correlation, and adding cross-sections where correlation is low, using a reaction-diffusion equation for region extraction and texture-based volume rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of cross-sections is reduced to decrease processing load, then processing load is reduced, but display accuracy deteriorates when volume data is rotated
Solution Approach 1:
The patent applies preliminary action by performing region extraction and correlation calculation on cross-sections before the actual rendering process. This allows the system to pre-identify and delete redundant cross-sections based on brightness value correlation, so that when rendering is performed with reduced cross-sections, the display accuracy is maintained because only truly redundant sections were removed. The preliminary analysis of brightness value patterns enables proactive optimization without sacrificing rendering quality.
2Productivity
If cross-sections are deleted based on brightness value correlation, then processing load is reduced, but image quality may deteriorate if important regions are removed
Solution Approach 1:
The patent applies local quality by performing region extraction that identifies specific regions of interest within each cross-section based on brightness values. Instead of treating all pixels uniformly, the method locally analyzes brightness patterns to determine which regions contain important information. When deleting cross-sections, only those with low correlation in their extracted regions are removed, preserving local image quality in important areas while allowing reduction in less critical regions.
3Measurement precision
If region extraction is performed for all cross-sections, then rendering accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies the extraction principle by selectively extracting regions from cross-sections based on correlation analysis rather than processing all cross-sections uniformly. The method extracts brightness value patterns from cross-sections, compares them to identify redundancies, and removes correlated sections before rendering. This selective extraction approach maintains rendering accuracy for non-redundant regions while significantly reducing the total processing time by avoiding unnecessary extraction and rendering of duplicate information.
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
This approach reduces processing loads and improves image clarity by selectively deleting or adding cross-sections, maintaining rendering quality and reducing computational burden.
Implementation Method 1
performing a reaction-diffusion equation including a diffusion element and a reaction element
Data Source
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AI summary
A disclosed method includes extracting a region from each of plural cross sections in a volume data representing a solid to be rendered, based on data of brightness values of texels for each of the plurality of cross sections, wherein the plural cross sections are perpendicular to an axis set for the volume data; deleting any one of two adjacent cross sections among the plural cross sections based on a correlation between a region extracted for one cross section of the two adjacent cross sections and a region extracted for the other cross section of the two adjacent cross sections; and rendering the solid by using data of the cross sections after the deleting.