Multimodal Supervoxel Segmentation

Versión 1.0.4 (6,7 MB) por MSoltaninejad

The algorithm used in this code is the modification of the method Simple Linear Iterative Clustering (SLIC) which was proposed by Achanta et

https://github.com/M-Soltaninejad/MultimodalSupervoxel

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Actualizado 2 jun 2019

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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.

Citar como

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.

Ver más estilos

MSoltaninejad (2026). Multimodal Supervoxel Segmentation (https://github.com/M-Soltaninejad/MultimodalSupervoxel), GitHub. Recuperado 16 enero, 2026.

Compatibilidad con la versión de MATLAB

Se creó con R2016b

Compatible con cualquier versión desde R2012a

Compatibilidad con las plataformas

Windows macOS Linux

Categorías

Más información sobre Image Segmentation en Help Center y MATLAB Answers.

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Versión	Publicado	Notas de la versión
1.0.4	2 jun 2019	Code description and details updated
1.0.3	31 may 2019	"find3.m" is added. "Supervoxel_3D_MultiProtocol.m" is updated so it runs faster and shows the output supervoxels.
1.0.2	31 may 2019	Upload a sample data (used in the paper)
1.0.1	30 may 2019	GitHub link added
1.0.0	30 may 2019		Descargar

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.

MLA	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.
APA	Soltaninejad, M., Yang, G., Lambrou, T., Allinson, N., Jones, T. L., Barrick, T. R., Howe, F. A., et al. (2018). Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Computer Methods and Programs in Biomedicine, 157, 69–84. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.cmpb.2018.01.003
BibTeX	@article{Soltaninejad_2018, doi = {10.1016/j.cmpb.2018.01.003}, url = {https://doi.org/10.1016%2Fj.cmpb.2018.01.003}, year = 2018, month = {apr}, publisher = {Elsevier {BV}}, volume = {157}, pages = {69--84}, author = {Mohammadreza Soltaninejad and Guang Yang and Tryphon Lambrou and Nigel Allinson and Timothy L Jones and Thomas R Barrick and Franklyn A Howe and Xujiong Ye}, title = {Supervised learning based multimodal {MRI} brain tumour segmentation using texture features from supervoxels}, journal = {Computer Methods and Programs in Biomedicine} }