Awesome
Pottslab
Pottslab is a Matlab/Java toolbox for the reconstruction of jump-sparse signals and images using the Potts model (also known as "piecewise constant Mumford-Shah model" or "l0 gradient model"). Applications include denoising of piecewise constant signals, step detection and segmentation of multichannel image.
-- See also the <a href="https://blogs.mathworks.com/pick/2017/12/07/minimizing-energy-to-segment-images-or-cluster-data/">Pick of the Week</a> on 
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Application examples
Segmentation of vector-valued images
- Supports segmentation of vector-valued images (e.g. multispectral images, feature images)
- Linear complexity in number of color channels
- Label-free: No label discretization required
Left: A natural image; Right: Result using Potts model
Texture segmentation using highdimensional curvelet-based feature vectors
Used as segmentation method in
- A. Breger et al., Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images, Eye (2017).
Joint image reconstruction and segmentation
- Applicable to many imaging operators, e.g. convolution, Radon transform, MRI, PET, MPI: only implementation of proximal mapping reuqired - Supports vector-valued data - Label-free: Labels need NOT be chosen a-priori
Left: Shepp-Logan phantom; Center: Filtered backprojection from 7 angular projections; Right: Joint reconstruction and segmentation using the Potts model from 7 angular projections
<!---   Texture segmentation using highdimensional curvelet-based feature vectors --->Denoising of jump-sparse/piecewise-constant signals, or step detection/changepoint detection
- L1 Potts model is robust to noise and to moderately blurred data
- Fast and exact solver for L1 Potts model
- Approximative strategies for severely blurred data
Top: Noisy signal; Bottom: Minimizer of Potts functional (ground truth in red)
Used as step detection algorithm in
- A. Nord et al., Catch bond drives stator mechanosensitivity in the bacterial flagellar motor, Proceedings of the National Academy of Sciences, 2017
- A. Szorkovszky et al., Assortative interactions revealed by sorting of animal groups, Animal Behaviour, 2018
Usage Instructions
Standalone usage from command line (only image plain image segmentation supported)
- Call "java -jar pottslab-standalone.jar input output.png gamma" where gamma is a positive real number, e.g. 0.1 (thanks to fxtentacle)
Installation for Matlab (all features usable)
Quickstart:
- Run the script "installPottslab.m", it should set all necessary paths
- For best performance, increase Java heap space in the Matlab preferences (MATLAB - General - Java heap memory)
- Run a demo from the Demos folder
Troubleshooting:
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Problem: OutOfMemoryException
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Solution: Increase Java heap space in the Matlab preferences (MATLAB - General - Java heap memory)
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Problem: Undefined variable "pottslab" or class "pottslab.JavaTools.minL2Potts"
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Solution:
- Run setPLJavaPath.m
- Maybe you need to install Java 1.7 (see e.g. http://undocumentedmatlab.com/blog/using-java-7-in-matlab-r2013a-and-earlier)
Plugins for Image Analysis GUIs
Parts of Pottslab can be used without Matlab as pure Java plugins
- Icy plugin - an interactive image segmentation plugin based on Pottslab (written by Vasileios Angelopoulos)
- ImageJ plugin - an ImageJ frontend for Pottslab (written by Michael Kaul)
References
- M. Storath, A. Weinmann, J. Frikel, M. Unser. "Joint image reconstruction and segmentation using the Potts model" Inverse Problems, 2015
- A. Weinmann, M. Storath. "Iterative Potts and Blake-Zisserman minimization for the recovery of functions with discontinuities from indirect measurements." Proceedings of The Royal Society A, 471(2176), 2015
- A. Weinmann, M. Storath, L. Demaret. "The L1-Potts functional for robust jump-sparse reconstruction" SIAM Journal on Numerical Analysis, 2015
- M. Storath, A. Weinmann. "Fast partitioning of vector-valued images" SIAM Journal on Imaging Sciences, 2014
- M. Storath, A. Weinmann, L. Demaret. "Jump-sparse and sparse recovery using Potts functionals" IEEE Transactions on Signal Processing, 2014