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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
A hybrid tissue segmentation approach for brain MR images
Tao Song1, Charles Gasparovic, Nancy Andreasen
1Radiology Department, Radiology Imaging Lab, University of California at San Diego, 3510 Dunhill Street, San Diego, CA 92121, USA. taosong@ucsd.edu
Medical & Biological Engineering & Computing
|August 29, 2006
Summary
A new hybrid algorithm enhances brain MRI tissue segmentation by incorporating partial volume effects using a modified probabilistic neural network (PNN). This method eliminates the need for training sets, proving effective and robust.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neuroscience
Background:
- Accurate brain magnetic resonance image (MRI) segmentation is crucial for diagnosing neurological disorders.
- Partial volume effects in MRI data complicate precise tissue classification.
- Existing segmentation methods often require extensive training datasets.
Purpose of the Study:
- To introduce a novel hybrid algorithm for improved brain MRI tissue segmentation.
- To address the challenge of partial volume effects in the segmentation process.
- To develop a method that does not require training sets.
Main Methods:
- A hybrid algorithm combining a probabilistic neural network (PNN) with a hierarchical scheme.
- Integration of weighting factors in the PNN summation layer to model partial volume effects.
- Utilizing a self-organizing map neural network and expectation maximization for parameter generation.
Main Results:
- The proposed algorithm effectively segments brain tissues in MRI scans.
- Demonstrated ability to account for partial volume effects during segmentation.
- Comparative analysis showed superior effectiveness and robustness over conventional methods.
Conclusions:
- The novel hybrid algorithm offers an effective and robust solution for brain MRI tissue segmentation.
- The method successfully mitigates challenges posed by partial volume effects.
- Elimination of training set requirements enhances the algorithm's practicality.
