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

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

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:

• 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