Dear Helmi,
I won't answer you in detail on your specific case as I didn't fully
understand the problem/design.
Ideally, if you have at least a subset of the same bones being
digitized by all the users/operators, you can check for your
hypothesis 1.
In some cases it might be possible to "remove" measurement error from
your data using the method presented in
Valentin AE, Penin X, Chanut JP, Sévigny JM, Rohlf FJ (2008) Arching
effect on fish body shape in geometric morphometric studies. Journal of Fish
Biology 73:623–638. doi:10.1111/j.1095-8649.2008.01961.x
That method and other approaches/issues are also discussed in my
recent review on the topic of measurement error
Fruciano C (2016) Measurement error in geometric morphometrics.
Development Genes and Evolution, 226(3):139–158. doi:
10.1007/s00427-016-0537-4.
which might be useful to you to understand what's possible to do in
your specific case.
I hope this helps,
Carmelo
Helmi Hadi <[email protected]> ha scritto:
Dear Morphometricians,
I have a weird problem and I was hoping someone could help me on this.
I have the same CT bone (n=400) with 14 landmarks (2 Single point and 12
sliding) larndmarked in IDAV Landmark by three different individuals
following a figure key table. Each individual landmarked 100+ different
bones. The figure key table has the shape of the bone, with the landmark
locations and the order of landmarking. All users were briefed by a single
person (user1) on how to extract the bone and landmark. All three
individuals were present throughout the entire procedure but the bone
segmentation and landmarking process was conducted individually.
When I combine all the data, I noticed that PC1 and PC2 graph has two
clusters. The effect is about 55% (as detailed in the eigenvalues below)
After further checking the classifiers in MorphoJ, the source of the
clustering is one person (user1) landmarked it is slightly differently
compared to the other two. I have checked the outliers tab and no glaring
outliers exists. As the sample size is big, the curve seem to be quite
normal.
Eigenvalues % Variance Cumulative %
1. 0.01603351 55.674 55.674
2. 0.00496222 17.230 72.904
3. 0.00224594 7.799 80.703
4. 0.00121085 4.204 84.907
5. 0.00076858 2.669 87.576
...
How to interpret the results for this kind of data? Things which come to my
mind are:
1. Maybe the bones for user1 are different compared to the other two users.
Or
2. User1 thinks the landmark location slightly different compared to others.
Or
3. User2 and 3 could not locate the landmark locations of user1.
Or
4. The landmarks selected are unreliable.
Ideally I would need few other people relandmark the entire set, but it is
not possible to do it now. Can anyone help shed some light on what is the
probable cause? Thank you.
Kind regards,
Helmi Hadi, PhD
School of Health Sciences, Universiti Sains Malaysia,
16150 Kubang Kerian, Kelantan, MALAYSIA
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Carmelo Fruciano
Postdoctoral Fellow - Queensland University of Technology - Brisbane,
Australia
Honorary Fellow - University of Catania - Catania, Italy
e-mail [email protected]
http://www.fruciano.it/research/
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