Therefore more thought usually needs to be given to what sizes make most sense. If applying a filter in 3D instead of 2D, it may seem natural to define it as having the same size in the third dimension as in the original two.īut for a z-stack, the spacing between slices is usually larger than the width and height of a pixel.Īnd if the third dimension is time, then it uses another scale entirely. Unfortunately, the Fourier method cannot be used for non-linear filters such as the median filter. However, when this is the case a whole other method can be used to get the same result using the Fourier transform – where the speed no longer has the same dependence upon the filter size. Not all linear filters are separable, and applying a large, non-separable linear filter can also be extremely time-consuming. Process ‣ Filters ‣ Gaussian Blur 3D… uses this approach. Several 3D filters are available under the Plugins ‣ Process ‣ submenu. This implies the reduction in noise is somewhat similar to that of applying a 5×5 filter (25 values), but with a little less blurring in 2D and a little more along the third dimension instead. Importantly, it then replaces each pixel by the average of 27 values, rather than 9. Many filters naturally lend themselves to being applied to as many dimensions as are required.įor example, a 3×3 mean filter can easily become a 3×3×3 filter if averaging across slices is allowed. This means no pixels will be clipped in the output (although rescaling and rounding will still occur). In 2D it’s enough to press Reset in the Brightness/Contrast window, but in 3D this will only work if the minimum and maximum values from the entire stack happen to appear on the current slice!įor this reason, it’s good practice to run Enhance Contrast… prior to reducing bit-depths of stacks, setting the saturation to 0 and using the entire stack. To minimize the information lost, these should usually be set to the minimum and maximum pixel values within the image – otherwise values will be clipped. So long as Normalize and Equalize histogram are not selected, the pixel values shouldn’t be changed.Ĭonverting bit-depths of multidimensional imagesĪs described in Types & bit-depths, the minimum and maximum display range values are used by default when reducing the bit-depth of an image. those that should be shown with the first or last colors in the LUT. You can also specify the percentage of pixels that should be saturated (clipped) for display, i.e. Process ‣ Enhance Contrast… is a better choice, since here you can specify that the information in the entire stack should be used. Optimizing the display for a single slice does not necessarily mean the rest of the stack will look reasonable if the brightness changes much. The normal Brightness/Contrast… tool only takes the currently-displayed slice into consideration when pressing Reset or Auto. Setting the LUT of a 3D image requires particular care. Other options, such as filtering and thresholding, are possible, but bring with them extra considerations – and often significantly higher computational costs. Image arithmetic involving a 3D stack and a 2D image can also be carried out in ImageJ using the Process ‣ Image Calculator, where the operation involving the 2D image is applied to each slice of the 3D stack in turn. Point operations are straightforward: they depend only on individual pixels, so the number of dimensions is unimportant. Point operations, contrast & conversion # This section gives a brief overview of some things to think about when working with z-stacks and time series in ImageJ. append ( './././' ) from helpers import * from matplotlib import pyplot as plt from myst_nb import glue import numpy as np from scipy import ndimage Introduction #
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |