elegans 3D reference data set also from the Biomedical Imaging Group ( ) Does anyone know how to do that in ImageJ or in Matlab? Is this just the central stack where the bead is in sharp focus? Or is it a maximum intensity projection of the stack? I have no idea.Īs reference data (and so we are talking about the same thing) I have been using the C. Secondly, I want to also generate 2D PSFs based on bead imaging (or theoretical PSF using objective and refractive index parameters) as well. Is there another step? Do I need to convert it to an optical transfer function? Am I missing something? I assume now that this 3D Array imported into Matlab is my PSF. (note: there may be an easier way to do this) So far, I have found that I can convert the TIFF to an HDF5 file (.h5) and import that with the following code: Once I have a 3D TIFF stack, I want to import this into MATLAB. Typically, I have been generating a TIFF stack of 0.1 micron bead, or a theoretical 3D PSFs using the excellent ImageJ plugin by Biomedical Imaging Group in Lausanne ( ).

I am familiar with constrained iterative deconvolution and have made 3D TIF stacks of beads before to generate PSFs in programs like Slidebook and Volocity etc.

I need the PSFs for testing deblurring algorithms. But we'll reach some constraint that'll fulfill our task.I am new to coding and image analysis in Matlab and attempting to import theoretical or measured (0.1 micron bead) point-spread-functions (PSFs) in to Matlab. We can never get a perfectly wrapped Output Image, since its size can be very large. By performing some Translation and Stuff, and taking a bigger Size Output Image, You'll get this. However you'll notice that Image is Transformed to get parallel Divider, as we Expected. You'll see a lot of Area is Cropped to fit the Output in InputImage Size. WarpPerspective(ipImg, opImg, TransformMat, ipImg.size()) Of opencv on initial and destination quadilateral Points, we get a Transform Matrix, Which we apply to our Test Image. Mat TransformMat = getPerspectiveTransform(ipPts, opPts) Remember, we are transforming our image such that the divider fits into this Rectangular Area. Now, for the Destination, we have to Image so that Divider Quadilateral has uniform width of 60, so we save in another array new points, which have same bottom points, but top points are modified as they form a rectangle with height same as image (or bigger, you can adjust as it suits you after running it once and seeing the results), but width 60. In my case, the width of road at top of image is 10, and at bottom is 60. I skipped this part by Manually Choosing them. Find out the extrema points that cross image boundary. Then, using Houghlines, find out the longest lines in Image.

Convert 3d image to 2d in matlab