Analyse the directionality of nanofibers

knime
orientation
Tags: #<Tag:0x00007fd5403ce520> #<Tag:0x00007fd5403ce368>

#1

Hi,

Can anybody tell the step wise process to analyse the directionality of nanofibers. I want to analyse the percentage of aligned fibers in my images.

Thanks in advance


Orientation-based quantification
#2

Hi @rsharma

If you have a segmentation you can compute the maximum Ferets angle (angle of maximum Feret diameter) and bin the angles.

Here (23.4 KB) a small workflow to get you started.


#3

Hi tibuch

Thank you for the reply. But i am unable o open the file which you shared. If you can share the workflow in comments here it will be very kind.

Thank You


#4

Sorry! My bad, it is a KNIME Image Processing Workflow.


#5

Thank You. But can you please clarify that by segmentation you mean the segmentation of images or the plugin?


#6

Also I am not familiar with coding and image processing so I dont know how to proceed through it after installing imagej plugin into KNIME.


#7

Please check: https://www.youtube.com/watch?v=4GiwmM-qcC4 how to import/export workflows. Hope this helps!


#8

I assume you have a segmentation and want to classify the individual region of interests (ROIs).


#9

@rsharma I split your post and the replies to a new topic, since your data seem to differ a bit from the images discussed in the original thread.

Why don’t you post some example images? This will help others to make useful suggestions.


#10

Thank you so much .

I am looking for the directional data for alignment of fibers. Can I get a quantitative value with deviation ?


#11

Good day Rama Krishna Sharma,

here are the orientation salience functions of windowed parts of your images:


An orientation salience function is a kind of histogram and may be normalized accordingly. In fact it is a weighted histogram, i.e. intensity/contrast of the structures are taken into account. For your SEM-images, the latter appears of minor influence, i.e. the orientation salience functions can be regarded as orientation histograms in the geometric sense.

If this is of any help for you, I shall point you to the relevant publications.

Regards

Herbie


#12

I think you should be careful with the analysis of this type of images. Whether you try the excellent and elaborate suggestion by @Herbie, or use more easily accessible options like Directionality or OrientationJ (as discussed also here and here), your measurements will be highly biased by the fibers in the foreground of your images, whereas the ones hidden in the back will have less influence on the final measurement.

This means that even if you had a number of uniformly distributed orientations, you might measure a predominant orientation with higher fiber density, just because this orientation is more frequent in the front. Just be sure to perform the appropriate controls.


#13

Salut Jan!

[…] your measurements will be highly biased by the fibers in the foreground […]

Absolute correct and an important aspect.

Best

Herbie


#14

Thank You very much. This helps a lot. I see in the articles where they state various quantitative results like angular deviation ,mean vector angle etc. to show the alignment. I know some of that data can be generated using directionality plugin in ImageJ. But is that data sufficient for the publication purpose. It will be great help if you can suggest something.

Thank you


#15

Thank you Very much. I get your point but i guess most of the algorithms work this way where they focus on foreground of image. It will be very kind if you can suggest what kind of control i can keep?

Also I tried doing the same analysis using directionality plugin of Image J and it gives me direction, dispersion, amount and goodness. What will be the good parameter to see the alignment dispersion or goodness?( I mean if we have to say that this sample has more aligned fibers as compared to other one what parameter we should consider) ?


#16

Good day Rama Krishna Sharma,

I fear you should first understand what “orientation histogram” means before you think of further descriptors.

BTW and as I’ve mentioned before, the orientation histograms generated by the “directionality”-plugin are a bit problematic.

Here are two papers that explain the theory and computation of “orientation salience functions”:
http://www.gluender.de/Writings/WritingsTexts/WritingsDownloads/1986_IntegralsCentralSlices.zip
http://www.gluender.de/Writings/WritingsTexts/WritingsDownloads/2013_Orientation.zip

Regards

Herbie


#17

Also, I tested the Directionality plugin in a couple of images and while I can see that in the images where the alignment is more prominent the distribution of directions becomes sharper (narrower gaussian fit), I don’t know how to say whether there are significant differences between different sets of images.


#18

Good Day Herbie

Thank you. I would definitely look into the articles which you have suggested.


#19

Good day Rama Krishna Sharma,

with regard to the “directionality”-plugin:
I’ve never understood the Gaussian fit and why it may be applied.
Do you have an explanation.

Please compare the results obtained with the “directionality”-plugin to the result I’ve previously posted and make sure you understand the idea of orientation histograms" per se.

Regards

Herbie


#20

Really nice figures, @Herbie! How did you create them? I should like to reproduce them locally, and try that approach on some of our collagen data.