MATLAB Implementation of Medical Image Segmentation Using Level Set Method
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Resource Overview
This is a custom implementation of the level set method for medical image segmentation, delivering excellent results. The code includes robust curvature-based evolution and adaptive time-stepping for stable convergence. Feel free to test it with your own datasets.
Detailed Documentation
In this implementation, I developed a customized level set method to achieve medical image segmentation. The algorithm utilizes partial differential equations (PDEs) for curve evolution, incorporating distance regularization to maintain stable level set function. Key features include:
1. Edge-based energy functional with Gaussian-smoothed gradient magnitude
2. Reinitialization procedure using Signed Distance Function (SDF)
3. Narrow-band optimization for computational efficiency
The method demonstrates particularly strong performance in segmenting anatomical structures from CT/MRI scans. Additionally, I've explored its applications in natural image processing (e.g., object boundary detection) and industrial inspection (defect identification in materials). The implementation uses MATLAB's PDE toolbox and image processing functions for efficient numerical computation. Hope this proves useful for your research or application development.
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