3D Image Segmentation for Similar-Intensity Structures
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Solution Overview
Problem
Existing digital data processing methods struggle to efficiently visualize and process image data sets, particularly in medical imaging and other fields, where structures with the same or similar grey values render indistinguishably.
Innovation Solution
The methods and apparatus described perform image segmentation in two- and three-dimensions to differentiate structures with the same or similar intensity values, allowing for distinct visualization and processing of these data sets. This includes identifying regions within 3D volumes, performing threshold segmentation, and using geometric characteristics to label connected components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If threshold-based segmentation is applied to separate structures by intensity values, then structures with different intensities can be distinguished, but structures with the same or similar grey values remain indistinguishable
Solution Approach 1:
The patent applies multi-dimensional segmentation by dividing the feature space into multiple dimensions including intensity, spatial location, geometric characteristics, and texture properties. This allows structures with similar intensity values to be differentiated through their distinct characteristics in other dimensions, such as shape, size, or spatial relationships.
Solution Approach 2:
The patent implements local quality analysis by examining specific regional characteristics within the image data. Different structures are identified based on their unique local properties such as curvature, thickness, and spatial distribution patterns, even when their overall intensity values are similar.
2Productivity
If automated image segmentation is performed to simplify image interpretation, then processing efficiency is improved, but the complexity of the segmentation algorithm increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing the image data to enhance relevant features before segmentation. This includes applying filters to emphasize structural characteristics, pre-identifying regions of interest, and preparing multi-dimensional feature representations that facilitate more straightforward segmentation operations.
Solution Approach 2:
The patent transitions from two-dimensional spatial analysis to multi-dimensional analysis by incorporating additional feature dimensions such as intensity gradients, texture properties, and spatial relationships. This dimensional expansion enables more effective segmentation without requiring overly complex algorithms, as the additional dimensions provide natural separation criteria.
3Measurement precision
If multiple segmentation criteria are applied to differentiate structures, then visualization accuracy is improved, but the time required for processing increases
Solution Approach 1:
The patent applies partial action by selectively applying different segmentation criteria to different regions or types of structures based on their specific characteristics. Rather than uniformly applying all possible segmentation methods to the entire image, the system applies appropriate criteria only where needed, reducing overall processing time while maintaining accuracy.
Data Source
AI summary
The invention provides methods and apparatus for image processing that perform image segmentation on data sets in two-and/or three-dimensions so as to resolve structures that have the same or similar grey values (and that would otherwise render with the same or similar intensity values) and that, thereby, facilitate visualization and processing of those data sets.