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MBIS: Multivariate Bayesian Image Segmentation tool | fP-CMC: Fast Patch-based Continuous Min-Cut segmentation

MBIS: Multivariate Bayesian Image Segmentation tool

An open source multivariate statistical n-classes clustering tool including graph-cuts optimization with specialization to brain Magnetic Resonance imaging.


Works using MBIS should cite:

Oscar Esteban, Gert Wollny, Sai Subrahmanyam Gorthi, María J. Ledesma-Carbayo, Jean-Philippe Thiran, Andrés Santos, Meritxell Bach Cuadra, MBIS: Multivariate Bayesian Image Segmentation tool, Comp Meth Prog Biomed 115(2):76–94, 2014.

DOI: 10.1016/j.cmpb.2014.03.003


You can download the code at https://github.com/oesteban/MBIS.




fP-CMC: Fast Patch-based Continuous Min-Cut segmentation

This software presents a semi-supervised segmentation framework for B-mode ultrasound imaging. It is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm.


Works using fP-CMC should cite:

Anca Ciurte, Xavier Bresson, Olivier Cuisenaire, Nawal Houhou, Sergiu Nedevschi, Jean-Philippe Thiran, Meritxell Bach Cuadra, Semi-Supervised Segmentation of Ultrasound Images based on Patch Representation and Continuous Min Cut. Plos One, Volume 9, Number 7, July 2014. DOI: 10.1371/journal.pone.0100972.


To download the code please click here.




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