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== Neuron Analyzer 2D ==
== Neuron Analyzer 2D ==


The ''Neuron Analyzer 2D'' is available since release version 1.1 of MiToBo. [[File:NeuronAnalyzer2D.png|frame]]
The ''Neuron Analyzer 2D'' is available since release version 1.1 of MiToBo. [[File:NeuronAnalyzer2D.png|right|link=]]


'''Name of Plugin:'''<br/>
'''Name of Plugin:'''<br/>

Revision as of 08:33, 16 February 2013

Several image processing pipelines have already been developed in MiToBo.
Below you can find selected example applications, some of them have been published already.

MiCA - MiToBo Cell Image Analyzer

presented at
B. Möller and S. Posch,
"MiCA - Easy Cell Image Analysis with Normalized Snakes".
Workshop on Microscopic Image Analysis with Applications in Biology (MIAAB '11), Heidelberg, Germany, September 2011.

Name of Plugin:
CellImageAnalyzer_2D (since MiToBo version 0.9.6)

Main features:

  • Integrated analysis of multi-channel microscope images of cells
  • Allows for segmentation of cells, nuclei and sub-cellular structures
  • Techniques subsume active contours, wavelets, morphological operators, and others
  • Visualization and quantitative summary of segmentation results

Scratch assay analysis

published in
M. Glaß, B. Möller, A. Zirkel, K. Wächter, S. Hüttelmaier and S. Posch,
"Scratch Assay Analysis with Topology-preserving Level Sets and Texture Measures".
Proc. of Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA '11), LNCS 6669, pp. 100-108, Springer, Las Palmas de Gran Canaria, Spain, June 2011.

Name of Plugin:
ScratchAssay_Analysis (since MiToBo version 0.9.5)

Description:

  • Quantifies the scratch area in monolayer cell culture images with levelset techniques
  • Combines the results from images of different time points in a results table


Example images
Scratch assay images

Neuron Analyzer 2D

The Neuron Analyzer 2D is available since release version 1.1 of MiToBo.

NeuronAnalyzer2D.png

Name of Plugin:
NeuronAnalyzer_2D (since MiToBo version 1.1)

Main features:

  • Neuron boundary detection based on active contours
  • Identification of structural neuron parts, like soma, neurites and growth cones
  • Morphology analysis, e.g., neurite length, average neurite width, number of branch and end points, growth cone size and shape roundness, etc.
  • Extraction of molecular profiles from fluorescently labeld molecules
  • Detection of molecular particles, for example FISH data

Example images
...will be available soon