I am using MeasureNeurons to, well… measure neurons, and was unsure about the role of the seed objects. If I take the branchpoint image and separate out the branch ends using Morph>endpoints and then use Math to divide this number by the number of nuclei in the image, I get a different result compared to the MeasureNeurons output “Mean_Soma_Neuron_NumberBranchEnds_Skel”

Do you know why this would be? The seed objects I use (soma) are slightly different from the nuclei. The pattern I get is the same, but the numbers are very different, and this is not accounted for by the (small) difference in number of nuclei compared to number of soma. Just wanted to check what would be different.

Thanks for your help.


There are three types of points in this “branchpoint” image (from … urons.html):

[quote]NumberTrunks: The number of trunks. Trunks are branchpoints that lie within the seed objects
NumberNonTrunkBranches: The number of non-trunk branches. Branches are the branchpoints that lie outside the seed objects.
NumberBranchEnds: The number of branch end-points, i.e, termini.[/quote]

Perhaps by your method (Morph > endpoints) starting with only the branchpoint image you are counting NumberTrunks also? You didn’t say which way the numbers skew, so this is just a guess.


Hi David,

Thanks for your reply - I just checked that and it is indeed the case that I’m including both.

Thank you!

OK great, thanks for reporting back.