Removing stitching lines in large images (image normalization)

Hi, I am working with confocal microscopy, imaging root of plants. Since the roots are big, I have to capture a large image (stitched image). This kind of images produces black lines at the stitching points. How I can get rid of these lines? And how I can normalize the image, I mean how I should treat the values of the px for correction and after correction (illumination correction)? Thanks, HM

[Hassan]

Dear Image Analyst,

I have series of root images (movie). For each one of the frames I have to do BackGround correction individually to the desired channel.

My questions is: This step will not affect the data (mainly intensity of fluorescent signal) extracted from each frame? (I mean, each one of the frames might respond differently to the correction)

*Could you please have a look to what I added below.

Best, HM

Answer 1

[Image Analyst]

You should do a shading correction before they get stitched. Take a blank shot and divide the image by it. Or model it and divide by the model as illustrated in my attached background correction demo.

[Hassan]

thank you, I have first to go over the .m file you attached. But for the blank image, I can't take it because of the root (i can't remove it), it is a dynamic procces and takes around 2 weeks, so the imaging conditions before the root development is not the same after 2 weeks. Thank you, HM

[Hassan]

the .m file use a "GetModelBackgroundImage" command! What is this command? you didn't mentioned to which package it belongs?. HM

[Image Analyst]

GetModelBackgroundImage() is not in a "package" - it's there in the m-file at line 190.

If you have access to the original, individual, unstitched images, you might be able to estimate a background by averaging all those images together and then fitting a smooth model to it like I did in the demo. Or maybe you can use the bottom third of the image, with no root in it, to do that.

[Hassan]

the attached image is one image of a long time laps imaging of different xy points. each root behaves differently and can go to different area in the large image, it is not always the last third.

The demo you attached requires deep understanding of different functions. I am currently investing more time in learning it. Sure I will have more questions and clarifications in the coming days.

last question, do I have to apply this process of correction to each fluorescent channel I acquired? (because the stitching affects also the other channels.)

Thank you a lot, HM

[Image Analyst]

What do you mean by "each fluorescent channel"? Like a color channel of an RGB image?

[Hassan]

Sorry for the late reply (I was away for few days). Fluorescent channel (mono chrome) contains a fluorescent details with different background from the bright field image but still have the effect of stitched process.

[Hassan]

Dear Image Analyst, I am attaching here the output after modifying the code for my images. The original images are uint16. I hope did well with the conversions, and I didn't miss any data for the final result.

Before I share with you the final result I got, I want to tell what the algorithm is doing in my words (please correct me if I am wrong)... 1. read and display the gray image 2. filter the objects in the image and display 3. generate BW image (white parts are not included in background calculation) 4. generate a model using polynomial fitting 5. correct the original gray image and show summary

*attached the results I got after modeling the code. *for filtering I used two step segmentation (the main root and the root hairs). The poly order is 3 (the best result I got... order 9 worked nice also, but I miss the root hairs that are not segmented well!)

Thank you, you helped me a lot, I am waiting to your feedback, Best,