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DOWNLOADSPseudo-diffusion and diffusion magnetic resonance tensor imaging

 

Pseudo-diffusion and diffusion magnetic resonance tensor imaging

 

 
 
 
Details
Description

The provided folder contains MATLAB codes which implement damped Gauss Newton algorithm with inexact line search for simultaneous pseudo-diffusion and diffusion tensor imaging using simulated diffusion magnetic resonance imaging data. The code solves a intravoxel incoherent motion diffusion tensor imaging (IVIM-DTI) model for estimation of the tensors and two other related parameters.

File Format

ZIP File

Platform

Windows, MAC, Linux

Size of download

13.9MB

Author

Meghdoot Mozumder, Department of Mechanical Engineering, The University of Sheffield
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Thursday, 24 September 2015 00:00

Sparse Statistical Shape Modelling

Written by
DOWNLOADSSparse Statistical Shape Modelling

 

Sparse Statistical Shape Modelling
SparseSSM

 

 
 
 
Details
Description

The provided code is based on the work by A. Gooya et al. [1] which proposes a Gaussian mixture model based approach to training statistical shape models (SSMs). The novel feature of the proposed approach is the application of a symmetric Dirichlet prior on the mixture coefficients to enforce sparsity and search over a continuous space for the optimal number of Gaussian components, to address the common issue of over or under-fitting. Additionally, we provide code to reconstruct surfaces from the unstructured point sets generated, following SSM training.

References

[1] Gooya, Ali, Christos Davatzikos, and Alejandro F. Frangi. "A bayesian approach to sparse model selection in statistical shape models." SIAM Journal on Imaging Sciences 8.2 (2015): 858-887.

File Format

TAR.GZ File

DOI

http://dx.doi.org/10.6084/m9.figshare.1562325

Platform

Linux

Size of download

3.33 MB

People who contribute to this work

Nishant Ravikumar, Department of Mechanical Engineering, The University of Sheffield
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Ali Gooya School of Computing, University of Leeds
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Alejandro F. Frangi School of Computing, University of Leeds
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