Image Segmentation Based on the Local Center of Mass

Versione 1.1.1 (7,32 KB) da Iman Aganj
Matlab codes for unsupervised 2D and 3D image segmentation, using a local-center-of-mass approach.
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Aggiornato 29 set 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.

Cita come

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. Recuperato .

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

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Creato con R2018b
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Versione Pubblicato Note della release
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