Apply background correction to a sequence
Hello, so I am trying to do a background subtraction to a sequence of deconvoluted images using the original sequence of images but when I do this, only the first frame from the original sequence is used to subtract as the background from the entire deconvoluted sequence.
What I am trying to do is process it so as to subtract (frame 1.orig) from (frame 1.deconv) then subtract (frame 2.orig) from (frame 2.deconv) and so on.
Is this possible, do I need to write a macro?
What I am trying to do is process it so as to subtract (frame 1.orig) from (frame 1.deconv) then subtract (frame 2.orig) from (frame 2.deconv) and so on.
Is this possible, do I need to write a macro?
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Best Answer
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The "Background Correction" function specifically uses a single background or flat field image to correct the target, so that's acting as it should. If you have an entire sequence of background images to subtract, use the "Process | Operations" dialog to subtract your background sequence image from your deconvolved sequence as an "image" operation.
I am, however, wondering a bit about your desired results. A deconvolved sequence will (a) not have much background and (b) I would expect the same background (due to camera issues) to apply to each frame. In fact, to get the best deconvolution results, background subtraction and flat fielding should be performed _before_ deconvolving, not after.0
Answers
Please see attached image.
I have a set of images that have been CT scanned at ~2.5mm per slice. I am trying to enhance the photo so that I can see only the cracking (traced by blue) and get rid of all the small dark speckers (circled in green) while retaining the fine detail of the cracks as they originally were (need to measure the volume of cracks after constructing the 3D image). I've tried using filters and combinations of the filters but they either blur the image too much or I lose too much detail by the time I've gotten rid of most of the specs. Any advice?
As a general rule, thresholding should be quite aggressive to get as many of your desired objects as possible, identifying more than you want, with detailed measurements and filters then cutting back the unwanted items. Being too conservative with the threshold identification means that you cannot recover missed objects. Oversegment then reduce.