Pseudo-diffusion and diffusion magnetic resonance tensor imaging
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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 FormatZIP File Platform
Windows, MAC, Linux Size of download
13.9MB Author
Meghdoot Mozumder, Department of Mechanical Engineering, The University of Sheffield |
Sparse Statistical Shape Modelling
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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 Ali Gooya School of Computing, University of Leeds Alejandro F. Frangi School of Computing, University of Leeds |