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BioVoxxel Toolbox

2,814 bytes added, 8 October
SSIDC Cluster Indicator
| source =[https://github.com/biovoxxel/BioVoxxel_Toolbox GitHub]
| category = [[:Category:Particle analysis|Particle analysis]], [[:Category:Binary|Binary]], [[:Category:Filtering|Filtering]]
}}You can setup an automatic update for the BioVoxxel Toolbox. Just run the [[Updater]] ({{bc | Help | Update...}}) and switch in the main window to 'Advanced mode'. Then click on 'Manage update sites'. Here you simply need to activate the "BioVoxxel" . See here how to follow an update site [[http://imagej.net/Following_an_update_site follow an update site]].
You will find all functions of the BioVoxxel Toolbox under the icon of the green BioVoxxel cube after selecting BioVoxxel Toolbox from the More Tools Icon (last Icon in the ImageJ/Fiji Icon list with the double arrow).
Example: If you want to extract/analyze only particles with a certain Feret's Angle or exclude elongated structures using the aspect ratio (AR) or circilarity you can specify so in the initial dialog box.
[[File:AdvancedParticleAnalyzerExtendedParticleAnalyzer v2.png|750px]]
How to: Key in minimal and maximal exclusion values connected with a hyphen. You can use integers as well as numbers containing decimal places. "Redirect" redirects the analysis to a grayscale image which enables to analyze skewness, kurtosis as well as the new measure coefficient of variance (cov). The option "Keep borders (correction)" eliminates particles from 2 edges and keeps particles touching the two borders of choice. This corrects the particle count for edge touching particles.
Coefficient of variance (CoV) = [intensity standard deviation] / [intensity mean]
 
 
'''The new version from 11th February 2018 (included from BioVoxxel_Plugins-2.0.1) on contains 2 checkboxes which enable the choice between pixel and calibrated units in images which are spatially calibrated. The checkbox "Pixel units" is equivalent to the checkbox with the same label in the standard IJ Analyze Particels... function and uses pixels instead of calibrated units to limit the analysis with the respective parameters. The checkbox "output in pixels" gives the option to separately choose if the results table should be in specific units or pixels (depending on the image calibration). Older Macros should still run, while the keyword "pixel" needs to be shifted before the definition of the area parameter.'''
 
Form: plugin (recordable, ''uses smart recording --> records only fields which have been changed by the user while recording. Default entries will not be recorded. It is already sufficient to cut a zero after the comma without changing the actual parameter value to make the recorder recognize that the entry should be recorded!'')
Status: Maintenance active (current version: 2.5.0)
Suggestions are welcome!
Potential issues: If the user does not correctly define if he/she is looking for bright or dark objects the output will be incorrectly determined.
'''Publication:''' [http://www.cscjournals.org/csclibrary/manuscriptinfo.php?ManuscriptCode=67.68.67.74.39.50.44.51.104&JCodemc=IJIP&EJCode=65.66.65.72.106&Volume=54.100&Issue=47.101 -829 Qualitative and Quantitative Evaluation of Two New Histogram Limiting Binarization Algorithms].
J. Brocher, Int. J. Image Process. 8(2), 2014 pp. 30-48
form of Pseudo Flat-field correction: plugin
Status: maintenance active, problems with specific stacks (fill be addressed soon)
Future: suggestions are welcome!
Form: macro
Status: maintenance active! Future: potentially also display using scaled units!deprecated
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Form: macro
Status: deprecated ----=Adaptive Filter= (separate plugin under {{bc | Plugins | BioVoxxel}}) Purpose/How to: This filter allows the choice between two basic filter modes (''median'' and ''mean''). The filter ''Radius'' defines the size of a square kernel (so actually not really a radius but to keep the entries intuitively similar to other filters in Fiji this label was chosen). The ''Shape'' option enables a basic pre-selection of pixels from the kernel neighborhood to be taken into account for filtering. After pressing 'Ok', a checkbox grid will be displayed from which the user can adjust the selected pixels for the final filter according to the filtering needs (e.g. to remove power lines in a photograph). ''Tolerance'' sets a threshold which will change intensity values after filtering only for those pixels where the difference to the original intensity is at least as high as the tolerance (0.2 = 20%). This enables to remove extreme outliers from the image while preserving the original pixel values in image areas without such outliers (at least for shot noise). In the image below the upper pannels show the original photograph and a version with artificial shot noise added. The lower pannels depict the noisy image after a median filter (radius=2) or after the Adaptive Filter (radius = 2 and tolerance set to 0.2) using a circle-like kernel. [[File:AdaptiveFilter.png]] Output: The filter will be applied directly on the input image. It is undoable (by pressing [z]). Limitation: So far, the filter is only applicable on 8-bit and 16-bit single images. Installation: Part of the BioVoxxel update site in Fiji and can be found under {{bc | Plugins | BioVoxxel}} Form: plugin, recording still not fully functional!!! Status: maintenance active!
Future: will Will be implemented for adjusted für 32-bit float-type images as wellas RGB images and stacks. Suggestions are welcome! ----=Recursive Filters= Purpose: The upper intensity limit recursive filters plugin allows to repetitively apply one of the three basic image filters (Gaussian Blur, Mean, Median) with small radii (max = 3) by adjusting the iteration. The previously filtered image will then be taken as basis for the next image filtering The maximum iteration can be set by the user up to 500 times but will be made individually adjustablestopped if two consecutive filtered images do not show any further difference. [[File:RecursiveFilters01. Suggestions welcome!png|500px]] Form: recordable plugin Status: maintenance active
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=SSIDC Cluster Indicator=
 
Meaning: Shape and size invariant DBSCAN based clustering
 
Pronounce: S-SID C like "acid" C ;-)
 
Purpose: Indicating clusters of binary image objects after segmentation based on a shape and size invariant density based clustering [https://de.wikipedia.org/wiki/DBSCAN DBSCAN]
 
Advantage and HowTo: the algorithm detects clusters of any size, shape and number! The user does not need to have any prior knowledge or estimation of a number of clusters (as for e.g. k-means).
The only input needed is the isotropic distance (in pixel) the user wants (epsilon in DBSCAN) to check around each object and a desired minimum density (number of objects inside the given distance). The algorithm reaches out equidistantly from the object shape. Therefore, amorphous shapes of particles do not matter. Furthermore, the size is not relevant, since the distance is taken from the outside of the object.
If size and shape should be ignored, the plugin can be run on the thresholded (min=1 max=255) ultimate points of the binary objects [►Process ►Binary ►Ultimate Points]
Clusters are also detected in an overlapping but still separate manner if some objects might belong to several clusters but do not belong to their core points.
 
Output: The output is on purpose very sparse and gives only the ImageJ ROI Manager. From there cluster numbers and individual amount of objects per cluster can easily be analyzed e.g. using a macro on the ROI manager and the corresponding image
 
Application: The algorithm was already successfully applied in the quantification of clusters from different cell populations in microscopic mouse retina samples [Link once the manuscript is accepted and published]
 
Limitation: works on 2D binary images only and does not detect clusters inside other clusters
=Cluster Indicator=
(depricated)
Purpose: The "Cluster Indicator" is thought to detect local particle clusters in a binary image. Different particle sizes can be taken into account.
Form: plugin, recordable
Status: maintenance active Future: Suggestions are welcome!stable, not further actively maintained
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Form: plugin, recordable
 
Status: maintenance active
 
Future: Will be adjusted für 32-bit float-type images as well as RGB images and stacks. Suggestions are welcome!
 
----
 
=Adaptive Filter=
(separate plugin under {{bc | Plugins | BioVoxxel}})
 
Purpose/How to: This filter allows the choice between two basic filter modes (''median'' and ''mean''). The filter ''Radius'' defines the size of a square kernel (so actually not really a radius but to keep the entries intuitively similar to other filters in Fiji this label was chosen).
The ''Shape'' option enables a basic pre-selection of pixels from the kernel neighborhood to be taken into account for filtering. After pressing 'Ok', a checkbox grid will be displayed from which the user can adjust the selected pixels for the final filter according to the filtering needs (e.g. to remove power lines in a photograph).
 
''Tolerance'' sets a threshold which will change intensity values after filtering only for those pixels where the difference to the original intensity is at least as high as the tolerance (0.2 = 20%). This enables to remove extreme outliers from the image while preserving the original pixel values in image areas without such outliers (at least for shot noise).
 
In the image below the upper pannels show the original photograph and a version with artificial shot noise added. The lower pannels depict the noisy image after a median filter (radius=2) or after the Adaptive Filter (radius = 2 and tolerance set to 0.2) using a circle-like kernel.
 
[[File:AdaptiveFilter.png]]
 
Output: The filter will be applied directly on the input image. It is undoable (by pressing [z]).
 
Limitation: So far, the filter is only applicable on 8-bit and 16-bit single images.
 
Installation: Part of the BioVoxxel update site in Fiji and can be found under {{bc | Plugins | BioVoxxel}}
 
Form: plugin, recording still not fully functional!!!
Status: maintenance active
Purpose: The mode-limited mean (MoLiM) and the differential-limited mean (DiLiM) are two binarization algorithms which initially limit the image histogram according to its mode value (MoLiM) or its mode, an initial mean or the median (DiLiM). A more detailed description can be found under:
[http://www.cscjournals.org/csclibrary/manuscriptinfo.php?ManuscriptCode=67.68.67.74.39.50.44.51.104&JCodemc=IJIP&EJCode=65.66.65.72.106&Volume=54.100&Issue=47.101 -829 Qualitative and Quantitative Evaluation of Two New Histogram Limiting Binarization Algorithms].
J. Brocher, Int. J. Image Process. 8(2), 2014 pp. 30-48
All BioVoxxel Macros were developed by {{Person|BioVoxxel}}
The macros can be freely used, redistributed and changed according to the GNU GPL BSD-3 License v3.0.
For questions, comments and suggestions please contact: jan.brocher(at)biovoxxel.de
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