Image Segmentation Based on the Local Center of Mass

Versión 1.1.1 (7,32 KB) por Iman Aganj
Matlab codes for unsupervised 2D and 3D image segmentation, using a local-center-of-mass approach.
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Actualizado 29 sep 2020

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These are codes for unsupervised 2D and 3D image segmentation, using an approach based on the local center of mass of regions, described in:

I. Aganj, M. G. Harisinghani, R. Weissleder, and B. Fischl, “Unsupervised medical image segmentation based on the local center of mass,” Scientific Reports, vol. 8, Article no. 13012, 2018.
www.nature.com/articles/s41598-018-31333-5

See EXAMPLE.m for a short tutorial. If available, a GPU can be used to speed up the segmentation.

Citar como

Iman Aganj (2024). Image Segmentation Based on the Local Center of Mass (https://www.mathworks.com/matlabcentral/fileexchange/68561-image-segmentation-based-on-the-local-center-of-mass), MATLAB Central File Exchange. Recuperado .

I. Aganj, M. G. Harisinghani, R. Weissleder, and B. Fischl, “Unsupervised medical image segmentation based on the local center of mass,” Scientific Reports, vol. 8, Article no. 13012, 2018. www.nature.com/articles/s41598-018-31333-5

Compatibilidad con la versión de MATLAB
Se creó con R2018b
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Versión Publicado Notas de la versión
1.1.1

Minor update.
1.1

In findCMs.m, the dimension through which the center of mass is computed is now adjustable and defaults to 1.
1.0.3

Minor update.
1.0.2

Minor update.
1.0.1

Minor update.
1.0.0