Changes between Version 2 and Version 3 of u/erica/Amira


Ignore:
Timestamp:
10/05/15 12:38:29 (9 years ago)
Author:
Erica Kaminski
Comment:

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  • u/erica/Amira

    v2 v3  
    11=== October 5, 2015 ===
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    3 Federath sent me a CDM of his turb+grav+mag+jets run. This was a fits file, whose pixel values represented column density in units of g/cm^2^, here is a picture of that is ds9:
     3Federath sent me a CDM of his turb+grav+mag+jets run. This was a fits file, whose pixel values represented column density in units of g/cm^2^. Ds9 showed the values ranged between 0 and ~2.5. Amira doesn't take fits files, but can process image formats. So I opened the fits in gimp and then exported it as a tiff file, and read this version into Amira. This apparently rescaled the data, as you can see from this histogram of voxel values:
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    5 [[]]
     5[[Image(FedHisto.png, 50%)]]
    66
    7 As you can see from the color bar, values range between 0 and about 2. Amira doesn't take fits files, but can process image formats. So I opened the fits in gimp and then exported it as a tiff file, and read this version into Amira. The scaling of the data is different now, as you can see from this histogram:
     7Now all the pixel values lay between 0 and about 66. This might have to do with the formatting of the tiff file (256 possible values in this format), but isn't overly important for skelatonization. If we wanted to do more in depth stats, might want to look into reading the data into Amira another way to preserve the absolute values of the voxels.
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    9 [[]]
     9The "auto" skeletonization method appears to take a threshold as an input. This threshold seems to 'mask' (or 'segment') the data so  to only look at voxels with values above this threshold. It then does a 'centerline' analysis, a 'distance map' analysis, and finally a 'thinning' of the skeleton so  that it is only 1 voxel across. To see the paper on this algorithm, it is attached to this page. It is also possible to do these 3 steps on your own in Amira, and thus gain more control over the skeletonization process.
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    11 Now all the pixel values lay between 0 and about 66. This might have to do with the formatting of the tiff file (256 possible values in this format), but isn't overly important for skelatonization. If we wanted to do more in depth stats, might want to look into reading the data into Amira another way so to preserve the absolute values of the voxels.
    12 
    13 The "auto" skeletonization method appears to take a threshold as an input. This threshold seems to 'mask' (or 'segment') the data so  to only look at voxels with values above this threshold. It then does a 'centerline' analysis, a 'distance map' analysis, and finally a 'thinning' of the skeleton so  that it is only 1 voxel across. To see the paper on this algorithm, it is attached to this page. One can go in with Amira and do these 3 steps on their own, and hence gain more control over the resultant skeleton.
    14 
    15 Here are some results testing different thresholds, and comparing them to Federrath's figure (the paper that contains this figure is attached to this page). Here is the figure we are comparing:
     11Here are some results testing different thresholds in auto skeletonization mode, and comparing them to Federrath's figure (the paper that contains this figure is attached to this page).
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    1713[[Image(compare5.png, 95%)]]
    … …  
    2319[[Image(comparept01.png, 95%)]]
    2420
    25 here are 3 different results:
     21I tested a couple smaller thresholds and the number of filaments seemed to converge to this value, N = 909.
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