Image Object Segmentation via Volumetric Interpolation
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Solution Overview
Problem
Existing methods for analyzing cardiac structures from image data struggle with accurately handling irregular structures and require significant user intervention, leading to increased costs and inaccuracies in measurements, especially for complex cardiac conditions.
Innovation Solution
A method is introduced that identifies and measures the volume of objects in images by interpolating contributions from data points within and outside the object, using techniques such as region growth and contour deformation to accurately define object boundaries, reducing the need for extensive user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to analyze cardiac structures from image data, then user intervention is required to handle irregular structures, but this leads to increased costs and inaccuracies in measurements
Solution Approach 1:
The system performs automated segmentation and volume measurement of cardiac structures without requiring user intervention. The computer executes algorithms that automatically identify object boundaries, handle irregular structures, and calculate volumes, making the system self-sufficient and eliminating manual operation requirements
Solution Approach 2:
The patent replaces manual mechanical tracing and measurement processes with automated computational algorithms. The computer-based system uses image processing techniques to automatically segment cardiac structures and calculate volumes, substituting human operators with automated software
2Manufacturing precision
If manual tracing methods are used to define object boundaries, then user input is extensive, but this increases the time required for analysis
Solution Approach 1:
The system automatically defines object boundaries through computational algorithms that analyze image data and identify structural limits without user input. The computer executes code that performs boundary detection and segmentation autonomously, eliminating the time-consuming manual tracing process while maintaining accuracy
Solution Approach 2:
Manual tracing operations are replaced with automated image processing algorithms. The computer-based system uses pixel analysis and edge detection techniques to automatically define boundaries, substituting the slow manual process with rapid automated computation
3Measurement precision
If complex algorithms are used to handle irregular cardiac structures, then measurement accuracy improves, but computational intensity increases
Solution Approach 1:
The patent segments the cardiac image into discrete voxels and identifies full-blood voxels versus partial-blood voxels. By dividing the complex irregular structure into manageable volumetric units, the system achieves accurate measurements through systematic processing rather than requiring excessively complex algorithms
Solution Approach 2:
The system transitions from two-dimensional image analysis to three-dimensional volumetric analysis by processing voxels. This dimensional approach naturally handles irregular cardiac structures by treating them as volumetric objects, achieving accuracy through geometric reasoning rather than complex computational algorithms
4Productivity
If automated methods are implemented to reduce user intervention, then operational efficiency improves, but handling irregular structures becomes more difficult
Solution Approach 1:
The system changes the analysis parameter from intensity-based to volume-based by processing three-dimensional voxels. This parameter transformation enables automated handling of irregular structures through volumetric calculations, achieving both operational efficiency and accurate measurement of complex cardiac geometries
Solution Approach 2:
The patent employs three-dimensional volumetric analysis using voxels to represent cardiac structures. This dimensional approach provides automated methods for handling irregular shapes by treating them as volumetric objects with definable boundaries, making irregular structure analysis tractable through systematic 3D processing
Data Source
AI summary
A method for identifying an attribute of an object represented in an image comprising data defining a predetermined spatial granulation for resolving the object, where the object is in contact with another object. In an embodiment, the method comprises identifying data whose values indicate they correspond to locations completely within the object, determining a contribution to the attribute provided by the data, and identifying additional data whose values indicate they are not completely within the object. The method next interpolates second contributions to the attribute from the values of the additional data and finds the attribute of the object from the first contribution and second contributions. The attribute may be, for example, a volume, and the values may correspond, for example, to intensity.