Automated Tissue Segmentation via Fuzzy Clustering
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Solution Overview
Problem
Current medical imaging techniques face challenges in accurately identifying and quantifying grey zone tissue due to overlap in contrast between healthy and unhealthy tissue types, sensitivity to coil calibrations, and reliance on precise timing of contrast administration, leading to tedious manual delineation and errors in segmentation.
Innovation Solution
An automated medical imaging system that uses multi-contrast magnetic resonance images and fuzzy clustering algorithms to classify tissue types independently of coil sensitivities and precise timing, allowing for accurate identification and measurement of grey zone tissue by generating measured value pairs from co-registered images and applying fuzzy c-means clustering for tissue segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual delineation is used to segment tissue types, then tissue classification can be performed, but the process becomes tedious and time-consuming
Solution Approach 1:
The system performs automated tissue segmentation using fuzzy c-means clustering algorithms that process MRI images independently without requiring manual intervention. The algorithm automatically classifies tissues into infarct, grey zone, and healthy myocardium based on signal intensity patterns, eliminating the need for tedious manual delineation while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces manual mechanical segmentation processes with automated computational image processing. The fuzzy logic-based algorithm substitutes human operators with an automated system that analyzes pixel intensity values and applies clustering rules to segment tissue types, significantly reducing time while preserving measurement precision
2Measurement precision
If multiple contrast settings are used to identify different tissue types, then tissue differentiation improves, but the segmentation process becomes more complex and tedious
Solution Approach 1:
The system utilizes changes in T1 relaxation parameters before and after contrast agent administration to differentiate tissue types. By measuring T1 values at different time points and applying fuzzy logic clustering to these parameter variations, the system achieves accurate tissue differentiation without requiring manual adjustment of multiple contrast settings or complex segmentation protocols
Solution Approach 2:
The patent introduces fuzzy logic clustering as an intermediary computational layer between raw MRI signal intensity data and final tissue classification. This intermediary process automatically interprets signal variations across multiple contrast settings and temporal points, translating complex multi-contrast data into clear tissue type classifications without requiring manual intervention
3Measurement precision
If conventional MRI sequences are used with precise inversion timing, then tissue identification can be achieved, but the method is sensitive to timing variations and coil calibrations
Solution Approach 1:
The system performs preliminary T1 mapping measurements before contrast agent administration to establish baseline tissue characteristics. By pre-characterizing tissue T1 values and using these as reference points for subsequent contrast-enhanced imaging, the system reduces sensitivity to timing variations and coil calibration differences, as the fuzzy logic algorithm can normalize across these variations
Solution Approach 2:
The patent employs dynamic T1 measurement at multiple time points (pre-contrast and post-contrast) rather than relying on a single static inversion time point. This dynamic approach allows the fuzzy logic clustering algorithm to identify tissue types based on temporal evolution patterns of signal intensity, making the method more robust to timing variations and coil calibration differences
4Measurement precision
If signal intensity-based segmentation is used, then grey zone can be identified, but noise in myocardium can be confused with grey zone
Solution Approach 1:
The system transitions from single-dimensional signal intensity analysis to multi-dimensional feature space by measuring T1 values at multiple time points (pre- and post-contrast). This dimensional expansion creates additional discrimination capability where noise and grey zone exhibit different temporal evolution patterns, allowing the fuzzy logic algorithm to reliably distinguish between them based on their distinct trajectories in the time-dimensional space
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
The system enables efficient and accurate classification of tissue types, reducing dependency on precise timing and coil calibrations, and provides rapid volume calculations, improving the identification of grey zone tissue and reducing noise impact on segmentation.
Implementation Method 1
quantitative T1 mapping identifies specific body tissues... T1 map... contrast enhancing... magnetic resonance techniques assess the area
Data Source
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Figure 3A~3D
AI summary
A medical imaging system (5) includes a data store (12), a clustering module (22), and a display device (32). The data store (12) includes a first imaging data set and a second imaging data set, each data set created with the same imaging device (10) and the same measured value. The measured value of a first and a second tissue type overlap in the first imaging data set. The measured value of the second and a third tissue type overlap in the second data set. The data sets are co-registered, and an external event changes the measured value of the second data set. The clustering module (22) classifies the tissue type based on a fuzzy clustering of the measured value of the first data set and the measured value of the second data set for each location. The display device (32) displays a medical image which contrasts each classified tissue type.