Commit cc4ffbf7 authored by Krzysztof Gonciarz's avatar Krzysztof Gonciarz
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Next increment of documentation - basics of DRS

parent 8152e8ef
Convolve with 2D Bessel function
This ImageJ plugin convolves 2D images or movies (image stacks) with a 2D Bessel
point spread function (PSF).
Algorithm Description and Manual
For better algorithm understanding please refer to `PDF <>`__.
......@@ -2,14 +2,52 @@
Discrete Region Sampling (DRS)
Discrete Region Sampling is a sampling version of well known Region Competition algorithm. It can be found in menu ``Segmentation > Discrete Region Sampling``.
.. figure:: resources/discreteRegionSampling/drs.png
:scale: 75 %
:align: center
Discrete Region Sampling (DRS) in action
Options shared with Region Competition
Most of parameters regarding initialization, geometric constrains (fussion, fission, handles) and energies are same as in Region Competition plugin. For their
description please refere to :ref:`regionCompetition-energy-parameters`.
Options specific for DRS
- **Number of iterations**
Number of iteration (sampling steps) to be performed. Test run of algorithm can be done with quite
small number of steps few hundred/thousand but for final run depending on size of a image it should be set to hundred thousands or even millions.
- **burn-in factor**
Fraction of all iterations called **burn-in** phase during which off-boundary probability helps to explore state space.
After **burn-in** phase off-boundary probability reduce to 0.
- off-boundary probability**
sets the off-boundary sampling probability
- **pair proposal**
enable mode in which each move corresponds to neighboring pair of particles
- **biased proposal**
enables biased-proposal mode in which in order to propose smoothed shapes
For detail explanation of these parameters please refere to `PHD thesis <>`__ desribing theory behind DRS.
Developer Resources
Source code and helpful information about MosaicSuite development can be found in :ref:`mosaicsuite-development` section.
.. admonition:: Citation
| *J. Cardinale*
| Unsupervised Segmentation and Shape Posterior Estimation under Bayesian Image Models. PhD thesis, Diss. ETH No. 21026, MOSAIC Group, ETH Zurich, 2013.
| `PDF <>`__
*In order to ensure financial support for our project and allow further development of
this software, please cite above publications in all your documents and manuscripts that
Poisson Noise
Due to the descrete character of light, images acquired using photon counting devices
such as CCD cameras suffer from *photon counting noise*: During the exposure time of a
given time *T*, a photon might or might not hit the sensor (since it is a non-continuous
event). Such a process can be modeled using a Poisson distribution.
This ImageJ plugin inserts Poisson distributed noise into an image or an imagestack to simulate
the acquisition of an image. The noise realization is independent for each pixel. The
distribution parameter *λ* is set to the original intensity value.
Algorithm Description and Manual
For better algorithm understanding please refer to `PDF <>`__.
PSF Measurement 2D/3D
With this tool you are able to estimate the PSF of a microscope setup and to compare it
to the theoretical PSF as calculated from elementary parameters of the microscope and
the camera. This serves two purposes: First, you might want to know whether you
adjusted your equipment optimally, and second, the PSF can be used for image restoration
purposes (in future versions of the plugin) such as deconvolution.
Algorithm Description and Manual
For better algorithm understanding please refer to:
- **2D** `PDF <>`__
- **3D** `PDF <>`__
......@@ -33,6 +33,7 @@ Output of a plugin:
- **Animated iteration sequence** Is the evolution of the segmentation produced by the algorithm, can be saved in all imageJ supported formats.
- **Statistics** Information about found regions like size, coordinates, mean intensity, label etc.
.. _regionCompetition-energy-parameters:
Energy function parameters
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