Tissue Detection Method Using Multi-Dimensional Analysis
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Solution Overview
Problem
Current imaging technologies face challenges in distinguishing between different objects with similar intensity values in complex 3D datasets, particularly in medical imaging, where it is difficult to isolate regions of interest from obstructing structures, and existing methods for tissue detection are inefficient in removing non-tissue structures like tagged objects.
Innovation Solution
A method that updates tissue probability volumes based on intensity, entropy, partial volume, and connectivity of voxels to generate a tissue volume, allowing for the isolation of tissue regions and removal of non-tissue structures, using a computer system to process datasets from scanners like CT, enabling effective computer-aided detection and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If intensity-based tissue classification is used, then tissue visualization is enabled, but objects with similar intensity values cannot be distinguished
Solution Approach 1:
The patent segments the tissue detection problem into multiple independent criteria: intensity-based classification, entropy-based classification, partial volume analysis, and connectivity analysis. Each criterion operates independently and contributes to the final tissue probability map, enabling distinction between objects with similar intensity values through additional dimensional analysis.
Solution Approach 2:
The patent transitions from single-intensity dimension to multi-dimensional analysis by introducing entropy, partial volume ratios, and connectivity as additional dimensions. This dimensional expansion allows differentiation of tissues that have overlapping intensity values through their unique combinations of these additional parameters.
2Quantity of substance
If 3D volumetric data is processed, then comprehensive tissue information is obtained, but processing complexity increases
Solution Approach 1:
The patent divides the complex 3D processing task into separate modular algorithms: intensity thresholding, entropy calculation, partial volume detection, and connectivity analysis. Each module processes specific aspects of the volumetric data independently, reducing overall processing complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent performs preliminary processing steps including initialization of tissue probability volumes and pre-calculation of entropy values for all voxels before the main tissue detection algorithm executes. This preliminary action reduces computational burden during the main processing phase and simplifies the overall algorithm execution.
3Ease of operation
If non-tissue structures are removed, then region of interest is isolated, but detection accuracy may be compromised
Solution Approach 1:
The patent uses dynamic probability thresholds and adaptive classification criteria that adjust based on the specific imaging data characteristics. The tissue probability map is generated through iterative refinement where connectivity and partial volume analysis dynamically adjust the classification of boundary regions, ensuring accurate tissue detection while achieving effective region isolation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the connectivity analysis and partial volume detection results feed back into the tissue probability map generation process. This feedback loop allows continuous refinement of tissue classification decisions, ensuring that region isolation does not compromise detection accuracy by validating each classification decision against multiple criteria.
Data Source
AI summary
A method for obtaining a tissue volume, includes inputting a dataset including a plurality of voxels; initializing a tissue probability volume for the plurality of voxels to a pre-determined value; updating, by one of increasing or decreasing the tissue probability volume of each of the plurality of voxels, based on corresponding intensity values of each of the plurality of voxels; and generating the tissue volume by combining the updated tissue probability volume and the inputted dataset.