Var branchLengths = skelResult.getAverageBranchLength() Var shortestPaths = skelResult.getShortestPathList().toArray() Var skelResult = n(AnalyzeSkeleton_.NONE, false, true, null, true, false) run(int pruneIndex, boolean pruneEnds, boolean shortPath, ImagePlus origIP, boolean silent, boolean verbose) (work on a copy of the ImagePlus if you don't want it displayed) Var imp = IJ.getImage() // get current open image ImportPackage(Packages.sc.fiji.analyzeSkeleton) Using the 3D_Viewer libraries we can easily display the results of both, the skeletonization and the analysis:

Analyze the resulting skeletons in the 3D image with AnalyzeSkeleton.

Extract the skeleton of a binary image with Skeletonize3D.

Pre-process a 3D image to extract the relevant morphological information by.

The tutorial describes step by step how to: Video tutorialДЇor a fast introduction to Skeletonize3D and AnalyzeSkeleton and an example of a real application, you can have a look at this video tutorial. The branches are sorted by decreasing length. This value has proven to be a good indicator of the tortuosity of the 3D object.

and the Euclidean distance between those extreme points.

3D coordinates of the extremes of the branch (the so-called V1 and V2 vertices),.

In this table we display all branches information: When calling the plugin, if the вЂњShow detailed informationвЂќ checkbox is marked, a complementary results table called вЂњBranch informationвЂќ is shown. Table of resultsД®xample of AnalyzeSkeleton Branch information window In those cases, a second call to the plugin is usually enough to eliminate all the remaining loops. The only known limitation of this approach is shown in the presence of nested loops.

After the edge classification, the BACK edges are backtracked following their predecessors in order to calculate all the edges belonging to each cycle and proceed with the pruning. While traversing the graph in the DFS fashion, the edges/branches pointing to unvisited nodes are marked as TREE edges, while the edges to visited nodes are marked as BACK edges, which involves the presence of a loop. The skeleton is treated as an undirected graph, where the end-points and junctions are the nodes and the slab-branches are the edges. The cycle detection is based on a classical вЂњDepth-First SearchвЂќ (DFS) in the skeleton.

lowest intensity branch: the darkest (in average) branch among the loop branches will be cut in its darkest voxel.ДЇor the two last methods, another dialog will pop up asking the user to select the original (gray-scale) image among the open images in order to perform the intensity calculations.

lowest intensity voxel: the darkest voxel among the loop voxels will be cut (set to 0) in the input image.

shortest branch: the shortest branch among the loop branches will be cut in its middle point.

none: no cycle detection nor pruning is performed.

In this sense, the initial plugin dialog offers 4 options: Since the 2 version of the code, the possible cycles or loops in the skeleton can be detected and pruned previous to the analysis. An extra output image will be displayed containing each skeleton labeled with its corresponding skeleton ID.

Show detailed info about the branches of each skeleton in the image.

In this case, the shortest path will be displayed in a new window containing the skeleton in white and the shortest path in magenta.

Calculate the largest shortest path of each skeleton using the APSP (all pairs shortest path).

An application of this feature is described in Strahler Analysis. If selected, pruning will not be applied to end-points contained by the ROI.

In this case, if a ROI was selected in the input image, another option is enabled: Exclude ROI from pruning.

Prune any branch that ends in an end-point (by checking вЂњPrune endsвЂќ), as implemented by Michael Doube in BoneJ.

Prune the possible loops in the skeleton (by choosing one of the pruning cycle methods).

In the main dialog of the plugin the user can select some options to Main dialog of the AnalyzeSkeleton plugin

Use imagej software for grain map