Why my image looks blurred

I got a stack of 2D slice images. I imported them as an image sequence. The first time I select auto brightness/contrast and display the image in ‘grays’. The result looks good. But this time I do the same and it looks blurry. I tried to make it look sharper using other functions available in imagej, but it says this action cannot be undo. So, I don’t want to take the risk.

The image is below. Any idea to solve this issue?

Hi Cnet,

I don’t know why your images are suddenly blurry, I assume you were zoomed in 800% in the first case as well?

If you want to test some operation on your image that cannot be rolled back you can duplicate the image and experiment on the duplicate.

Ctrl + shift + d or Image 》 duplicate.

Hi Sverre,

Thanks for your response. Yes, the image attached above was zoomed in but it was blurred even before I zoomed it in.

My goal was to select one of the parietal lobes on the brain and get the information such as the area of the selected object (mm^2), the mean number and std. deviation, etc., using the ctrl M command.

With the blurred image as above, it certainly won’t give me the accurate values. Using the other image functions may increase the sharpness for viewing but it may change the aforementioned information.

I agree that processing it to increase sharpness is not a good idea.

So, just to make sure I understand you. The first time you opened the image sequence they were clear, then you reimported the same image sequence and the images were blurry?

Can you share some raw sample images on this forum?

Hi cnet,

Did you accidentally change scale during the import as an image sequence? There is an option available to change scale in the window that pops up. I say this because your images are only 144x144 pixels by the looks of it, and your object of interest looks to take up a small proportion of the overall canvas size.

Is 144x144 pixels the original acquisition resolution?

Best,

Rob

A field of view of that size is quite typical for PET imaging. The spatial resolution / pixel size is usually something about 5mm.

Cheers,
Robert

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Hi Sverre,

I uploaded that particular image I was looking at from that image sequence on the link below.

Yes, as I remember the first time I imported those images as an image sequence, the images were a lot more clear than they are now. And the values I got also make sense.

Best Regards,

Well, I wouldn’t expect them to be more “clear” at a resolution of 144x144 at 800% zoom than what I see here. Did you apply any preprocessing at all when you opened them previously?

@haesleinhuepf maybe has some more experience with this kind of imaging, does this look normal to you?

Hi @Sverre & @cnet ,

sure, PET images look like this, especially if you look at them with a software which does not interpolate between pixels. These days, PET scanners don’t exist alone, usually combined PET-CT scanners are available in the hospitals. Furthermore, PET images are usually taken together with CT images (at least for attenuation correction). Physicians draw contours (e.g. for therapy planning) usually on the CT images with the PET images fused. In typical clinical software, both PET and CT images are shown with a trilinear interpolation. But finally, contours are saved in the coordinate system of the CT images (at least in most radiotherapy treatment planning software I have seen).

To make a long story short: What about upsampling the images (ImageJ Menu Image > Scale) with tri-linear interpolation to fit the voxel size of the corresponding CT image stack (usually something like 1.5x1.5x5 mm)? That does not eliminate the issue of being not precise, but PET scanners do have a “bad” resolution, so there may be no easy way to deal with that otherwise…

BTW: If you use an automatic thresholding approach to measure the volume of the area with high activity (PET signal), it may result in volume measurements which are mostly independent of if you use the liniear interpolated images or the original. The contours just look nicer in the interpolated case :wink:

Cheers,
Robert

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