# VolumeFinder
Procedures for volume analysis and spatial statistics of 3D point sets in IgorPro.

Two pairs of FIJI/Igor scripts represent two workflows to move segmented models from Amira into Igor and analyse them.

1. <code>VolumeFinder.ipf</code> & <code>amThreshTiff.ijm</code>
2. <code>FindingVectorsFromSkeleton.ipf</code> & <code>am2skel.ijm</code>

###Volume analysis
This code is to measure the density of microtubules in a stack of TIFFs. Microtubules are first segmented in Amira and then converted to TIFFs using a FIJI macro. Finally, Igor will work out the volume of the microtubules as a density of the volume in which they are contained. This was written for analysis of segmented data from SBF/SEM (3View). 

1. Segmentation is first done in Amira. Segmented microtubule labels have the value 2.
2. Amira files are thresholded and converted to TIFF in FIJI using the [amThreshTiff.ijm](https://github.com/quantixed/VolumeFinder/blob/master/amThreshTiff.ijm) macro
3. These TIFFs are batch-processed by Igor

To do this call <code>VolumeFinder(2)</code>. Option 2 specifies the fastest calculation method. Now point Igor at the directory containing the TIFFs.
You can scale the output to real world values using <code>ScaleIt(xnm,ynm,znm)</code>. This will scale the point volumes and hull volumes to µm^3.

Caution:
* For best performance /VOL flag is used, only available in Igor 7 Beta 6
* Code will compile in Igor 6.3+ but will use a slower method
* Option 0 is the most straightforward, but is very slow. Benchmarking with <code>tic()</code> <code>toc()</code> timed a complicated data set (768 x 768 x 500, 1.2 x 10^6 points) at ~3 h on a Mac Pro 6 Core. Option 2 speeds this to ~90 s.

###Spatial Statistics
Again Amira mesh files are used as a starting point. The FIJI script [am2skel.ijm](https://github.com/quantixed/VolumeFinder/blob/master/am2skel.ijm) will process these to first threshold them and make a directory of TIFFs and then produce categorised 1 px thick MT trajectories in 2D (one for each z-slice). These are called skeletons.

Skeletons are processed by Igor <code>FindingVectorsFromSkeleton.ipf</code> to form 2D vectors which can then be used for spatial statistical analysis. Vectors are found by a linear fit to xy coordinates.

Igor will produce a report which shows three spatial statistics:

* A comparison of all MT vectors with the spindle axis (defined by two xyz coords at the start of the procedure). This is colour coded (with a key) to show variance in angle from the spindle axis. Histograms summarise this information (4 hitsograms: 1 for each pole, 1 for all angles, 1 for all angles, abs() values).
* Two further histograms compare MTs that are longer than 60 nm are within 80 nm of other MTs. This is independent of the spindle axis.
* A comparison between the trajectories of MT segments with ellipsoid tangents calculated from an idealised spindle.

Each cell/movie is analysed as a separate pxp. Use [SummaryPXP.ipf](https://github.com/quantixed/VolumeFinder/blob/master/SummaryPXP.ipf) to make a summary report of all your data.

####Extra code

A little tool called `checkAxis.ipf` was developed to help visualise and correct spindle axis. This is not intended for general use.

A toy called `SimulateEllipses.ipf` was developed to model idealised spindles as ellipsoid tangents.