Multimodal Supervoxel Segmentation

Versione 1.0.4 (6,7 MB) da 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

Segui

5.0

(1)

509 download

Aggiornato 2 giu 2019

Visualizza la licenza su GitHub

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.

Cita come

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.

Visualizza più stili

MSoltaninejad (2025). Multimodal Supervoxel Segmentation (https://github.com/M-Soltaninejad/MultimodalSupervoxel), GitHub. Recuperato ottobre 24, 2025.

Compatibilità della release di MATLAB

Creato con R2016b

Compatibile con R2012a e release successive

Compatibilità della piattaforma

Windows macOS Linux

Categorie

Scopri di più su Image Segmentation in Help Center e MATLAB Answers

Tag Aggiungi tag

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate

Versione	Pubblicato	Note della release
1.0.4	2 giu 2019	Code description and details updated
1.0.3	31 mag 2019	"find3.m" is added. "Supervoxel_3D_MultiProtocol.m" is updated so it runs faster and shows the output supervoxels.
1.0.2	31 mag 2019	Upload a sample data (used in the paper)
1.0.1	30 mag 2019	GitHub link added
1.0.0	30 mag 2019		Scarica

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.

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} }