Multimodal Supervoxel Segmentation
The algorithm used in this code is the modification of the method Simple Linear Iterative Clustering (SLIC) which was proposed by Achanta et
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The algorithm used in this code is the modification of the method Simple Linear Iterative Clustering (SLIC) which was proposed by Achanta et al. (2012).
Our method is optimized for medical images such as MRI, CT, etc. The contributions of our codes compared to conventional 2D and 3D superpixel are as follows:
• Multi-modal input (works for single-modal, as well)
• Taking the spatial resolution of the medical images into account, i.e. the voxel resolution in X and Y directions and the slice thickness.
Cite As
Soltaninejad, Mohammadreza, et al. “Supervised Learning Based Multimodal MRI Brain Tumour Segmentation Using Texture Features from Supervoxels.” Computer Methods and Programs in Biomedicine, vol. 157, Elsevier BV, Apr. 2018, pp. 69–84, doi:10.1016/j.cmpb.2018.01.003.
MSoltaninejad (2026). Multimodal Supervoxel Segmentation (https://github.com/M-Soltaninejad/MultimodalSupervoxel), GitHub. Retrieved .
General Information
- Version 1.0.4 (6.7 MB)
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View License on GitHub
MATLAB Release Compatibility
- Compatible with R2012a and later releases
Platform Compatibility
- Windows
- macOS
- Linux
Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.4 | Code description and details updated |
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| 1.0.3 | "find3.m" is added.
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| 1.0.2 | Upload a sample data (used in the paper) |
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| 1.0.1 | GitHub link added |
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| 1.0.0 |
