Analyzing cell layer thickness

retina
Tags: #<Tag:0x00007fd69bbf2308>

#22

Hi @etarena
So I already made it through the workshop and managed to get the total area of the cell layer (ROI) and the longest shortest path.
What I’m looking for as a result is the thickness along the longest shortest path, for example to get an array of each measured point along the path from left to right and to extract the distance from that point along the path to the border of the ROI, so I could ultimately draw a graph of the thickness distribution along the cell layer…


#23

@ofirforsht

Sorry! My brain was still waking up… I misread your post.

Let me take a look at what @imagejan did in this old post to see if I can tease it out of there (haven’t done it myself yet!).

But you are also asking in this thread for this solution - yes??

So… let me know if you just want to go that direction instead - seems like @iarganda is getting you there.

eta


#24

Good day Ofir Forsht,

here is my result concerning your second sample image:

Thickness in pixels determined in a 16pixel wide running window (black) and polynomial fit (blue):

The method I’ve used is only applicable if the layers are rather straight.

Regards

Herbie


Corrosion Thickness Measurement
Measuring distance between two lines
#25

Hi @etarena
Yes, I tried my luck in a few threads…I got quite far the other way but not there yet.
The thread you mentioned seems relevant but honestly I didn’t understand what they did or how to implement the new Analyze_skeleton they created on my project.
Still need your help if you could figure it out.
Thank you,
Ofir


#26

Hi @Herbie,
Thanks for your help, but the layer I’m checking is almost never straight and sometimes can even have a twist in the middle so not sure if this is applicable in my case…
Let me know if you think otherwise.
Thanks anyway,
Ofir


#27

Dear Ofir,

it’s always difficult to help if the posted sample images are not representative for the task in question.

I showed a solution for one of your samples (that appears to be taken from the literature) and I really should like to know if the result is what you were looking for.

Regards

Herbie


#28

Hello @Herbie,
First of all, thank you for your help.
I tried to use polynomial fit and couldn’t get the results you got, didn’t even had an idea on what to choose in the X/Y axis degree…I might need a step by step instructions. hopefully we are talking about the same plugin - http://www.optinav.info/Polynomial_Fit.htm

Regarding the not representative sample remark - the images I attached are representative, it is the less common images that I am afraid of.
As the person who made all the steps to generate the images from a full animal (not taken from the literature BTW) I know that variation in staining intensity, sample integrity, folds and twist during histological slide preparation and a few more factors can create less than ideal samples. I’m attaching a few of them so you could see for yourself.
I am looking for a robust method that can be applied on all samples with minimum change in each image analysis to decrease variation, this is why I addressed your remark regarding the requirement for a straight sample as a problem.
Regards,
Ofir


#29

Good day Ofir,

thanks for the details and the images!

Just a few comments:

  1. I tried to create a series of processing steps that finally led from the second of your initially posted sample images to the result image and width data shown in my previous post.
  2. To create this series of ImageJ macros took me about a day (actually much more than eight hours though). There is no relation to the plugin you’ve mentioned.
  3. The main processing steps are:
    a) Rotational alignment of the image
    b) Segmentation of the layers
    c) Width analysis of the target layer
  4. I was heading for the most accurate width measurements possible, i.e. I’ve tried to avoid lowpass filtering as much as possible.
  5. The greatest problem was with step b) and I know that my approach doesn’t generalize well, even if the layers are rather straight. You’ve mentioned preparation problems (smearing etc,) that I’m aware of.
  6. My impression that the image comes from the literature is based on a Fourier-spectral analysis used in step a) that indicates the presence of a pronounced high frequnecy periodic structure in the image that may be related to a printing raster. However, it may also have different causes (did you do image stiching?).
  7. Meanwhile, I explored another approach that you may have already seen on this Forum: http://forum.imagej.net/t/accessible-surface/8202/4?u=herbie
  8. This approach is much more universal and robust and doesn’t require approximately straight layers; but the problem regarding layer separation remains (you’ve just posted a request that concerns this issue).
  9. Regarding good layer separation, I don’t see a straightforward automatic solution.

Regards

Herbie


#30

Hi @Herbie,
Thanks for you elaborated reply.
I can say with good confidence that cell layer segmentation is not an issue for my anymore. Using either Weka segmentation or color segmentation I generate a very good segmentation of My ROI, other than the overlapping layers I have mentioned and you’ve answered - the manual correction will actually be a good segmentation QA step in my analysis.
My concern now is how to create the cell layer thickness along the entire layer so I would love to hear about the new approach you mentioned in comment #7 (I saw no details on how to do it). I am sorry in advance, I’m a newbie in imageJ and java so you should probably take me step by step in your explanations…
BTW I attached the original images taken with Nikon eclipse 80i microscope, so I have no clue how could the high frequency periodic structure you mentioned be formed.
Thanks,
Ofir


#31

Well Ofir,

that cell layer segmentation is no longer a problem is somehow astonishing for me but maybe due to the fact that you are happy with approximate segmentations. The effort doesn’t scale linearly with precison/accuracy and perhaps my picture of the goal was different.

[…] approach you mentioned in comment #7

The approach is that proposed by the original poster of the thread, i.e. not exactly, because it is only the first step.

Applying this method to binary images is possible with existing ImageJ-plugins as demonstrated by the original poster in his last post of the thread: http://forum.imagej.net/t/accessible-surface/8202/6. A schematic result is shown in his image C.

My approach however works with the original image data, i.e. without thresholding. The code is experimental and not for distribution, but I’m sure you will find someone who is able to write the necessary code, or even better: Do it yourself.

Regards

Herbie