Applications: Difference between revisions
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Below you can find selected example applications, some of them have been published already. | Below you can find selected example applications, some of them have been published already. | ||
== | == Neuron Analyzer 2D == | ||
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/> | ||
NeuronAnalyzer_2D (since MiToBo version 1.1) | |||
'''Main features:'''<br/> | '''Main features:'''<br/> | ||
* | * 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 | |||
'''Note:''' The application is currently only available on Linux OS. | |||
'''Example images'''<br/> | |||
...will be available soon | |||
== Scratch assay analysis == | == Scratch assay analysis == | ||
| Line 36: | Line 39: | ||
[http://www.informatik.uni-halle.de/mitobo/downloads/scratch_examples.zip Scratch assay images] | [http://www.informatik.uni-halle.de/mitobo/downloads/scratch_examples.zip Scratch assay images] | ||
== | == MiCA - MiToBo Cell Image Analyzer == | ||
presented at<br/> | |||
''B. Möller and S. Posch,<br/> | |||
'''"MiCA - Easy Cell Image Analysis with Normalized Snakes"'''.<br/> | |||
Workshop on Microscopic Image Analysis with Applications in Biology (MIAAB '11), Heidelberg, Germany, September 2011.'' | |||
'''Name of Plugin:'''<br/> | '''Name of Plugin:'''<br/> | ||
CellImageAnalyzer_2D (since MiToBo version 0.9.6) | |||
'''Main features:'''<br/> | '''Main features:'''<br/> | ||
* | * 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 | ||
Revision as of 10:22, 21 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.
Neuron Analyzer 2D
The Neuron Analyzer 2D is available since release version 1.1 of MiToBo.
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
Note: The application is currently only available on Linux OS.
Example images
...will be available soon
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
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
