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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 8ea6a4271431c05c82b09c0d3e629b13e6ea7936
author iuc
date Fri, 22 Jul 2016 23:10:42 -0400
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1 <?xml version='1.0' encoding='UTF-8'?>
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2 <tool id="imagej2_skeletonize3d" name="Skeletonize" version="@WRAPPER_VERSION@.0">
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3 <description></description>
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4 <macros>
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5 <import>imagej2_macros.xml</import>
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6 </macros>
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7 <expand macro="fiji_requirements" />
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8 <command>
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9 <![CDATA[
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10 python $__tool_directory__/imagej2_skeletonize3d.py
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11 --input "$input"
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12 --input_datatype $input.ext
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13 --black_background $black_background
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14 --jython_script $__tool_directory__/imagej2_skeletonize3d_jython_script.py
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15 --output "$output"
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16 --output_datatype $output.ext
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17 ]]>
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18 </command>
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19 <inputs>
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20 <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="input" type="data" label="Select grayscale image"/>
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21 <expand macro="black_background_param" />
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22 </inputs>
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23 <outputs>
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24 <data name="output" format_source="input" label="${tool.name} on ${on_string}"/>
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25 </outputs>
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26 <tests>
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27 <test>
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28 <param name="input" value="blobs.gif" />
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29 <param name="input_datatype" value="gif" />
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30 <param name="output_datatype" value="gif" />
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31 <output name="output" file="skeletonized_blobs.gif" compare="sim_size" />
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32 </test>
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33 <test>
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34 <param name="input" value="clown.jpg" />
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35 <param name="input_datatype" value="jpg" />
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36 <param name="output_datatype" value="jpg" />
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37 <output name="output" file="skeletonized_clown.jpg" compare="sim_size" />
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38 </test>
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39 </tests>
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40 <help>
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41
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42 .. class:: warningmark
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43
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44 @requires_binary_input@
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45
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46 **What it does**
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47
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48 <![CDATA[
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49 Skeletonizes a 2D or 3D binary (8-bit) image. As Hanno Homman explains in his paper, binary thinning is
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50 used for finding the centerlines (”skeleton”) of objects in the input image. The general idea is to erode
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51 the object’s surface iteratively until only the skeleton remains. Erosion has to be performed symmetrically
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52 in order to the guarantee medial position of the skeleton lines and such that the connectedness of the
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53 object is preserved. Care has to be taken in order not to create holes or cavities in the object.
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54
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55 There are two major approaches to image thinning: a) kernel-based filters and b) decision trees. Kernel-based
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56 filters apply a structuring element to the image and can generally be extended to dimensions higher than 3D,
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57 to find computationally efficient solutions for 4D and higher dimensions is subject of ongoing research.
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58 Methods based on decision trees are thus far limited to 2D and 3D, but are potentially faster than morphological
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59 filters, if they are well designed and can find more deletable points at each iteration.
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60
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61 In 3D there are 67,108,864 possible binary combinations of object and background voxels in a 26-neighborhood,
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62 which cannot be completely captured by kernel-based filters. Lee et al. have demonstrated in their work that
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63 their solution, based on a decision tree, can handle all these cases correctly and find all deletable surface
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64 points at each iteration. Thus their algorithm allows for a very fast iterative erosion process.
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65 ]]>
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66 </help>
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67 <citations>
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68 <citation type="doi">10.1006/cgip.1994.1042</citation>
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69 <citation type="doi">10.1038/nmeth.2102</citation>
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70 </citations>
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71 </tool>