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 Pick of the Week on —

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

Vector-valued segmentation

Left: A natural image; Right: Result using Potts model

Vector-valued segmentation

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

Phantom
Phantom
Phantom

Left: Shepp-Logan phantom; Center: Filtered backprojection from 7 angular projections; Right: Joint reconstruction and segmentation using the Potts model from 7 angular projections

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

Phantom

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:

Problem: OutOfMemoryException
Solution: Increase Java heap space in the Matlab preferences (MATLAB - General - Java heap memory)
Problem: Undefined variable “pottslab” or class “pottslab.JavaTools.minL2Potts”
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