Image Filters¶

There are several filtering methods implemented in ImgArray.

import impy as ip

img = ip.random.random((5, 32, 64, 64), axes="tzyx")
img

ImgArray of
      name      : random
     shape      : 5(t), 32(z), 64(y), 64(x)
  label shape   : No label
     dtype      : float32
     source     : None
     scale      : ScaleView(t=1.0, z=1.0, y=1.0, x=1.0)

Batch Processing ¶

By default, ImgArray consider ["z", "y", "x"] axes as spatial axes and iterate functions along other axes. In this example, 3-D filter will be applied for every "t". If you want other iteration options, explicitly specify dims keyword argument.

img.gaussian_filter(sigma=2.0)
img.gaussian_filter(sigma=2.0, dims="yx")  # ["y", "x"] is considered as spatial axes.
img.gaussian_filter(sigma=2.0, dims=["y", "x"])  # same as dims="yx".

Denoising ¶

Gaussian filter ¶

Gaussian filter is a widely used denoising filter. It blurs image using Gaussian kernel. sigma is standard deviation (in pixel) of the kernel.

img.gaussian_filter()  # sigma=1.0 by default
img.gaussian_filter(sigma=2.0)  # use sigma=2.0
img.gaussian_filter(sigma=[2.0, 1.0, 1.0])  # non-uniform sigma

Median filter ¶

Median filter is a denoising that is considered to be robust against outliers. Kernel shape is specified by radius argument.

img.median_filter()  # radius=1.0 by default
img.median_filter(radius=3.2)
img.median_filter(radius=[3.2, 1.0, 1.0])  # non-uniform radii

Mean filter ¶

Mean filter (uniform filter) is a simple denoising, where image is locally averaged with same weight.

img.mean_filter()  # radius=1.0 by default
img.mean_filter(radius=3.2)
img.mean_filter(radius=[3.2, 1.0, 1.0])  # non-uniform radii

Use Standard Deviation ¶

Standard deviation filter ¶

Standard deviation filter can detect regions that signal changes a lot.

img.std_filter()  # radius=1.0 by default
img.std_filter(radius=3.2)
img.std_filter(radius=[3.2, 1.0, 1.0])  # non-uniform radii

Coefficient of variation filter ¶

Coefficient of variation is a quantity that is defined by S.D. / mean. Coefficient of variation filter is similar to standard deviation filter but is not sensitive to mean intensity.

img.coef_filter()  # radius=1.0 by default
img.coef_filter(radius=3.2)
img.coef_filter(radius=[3.2, 1.0, 1.0])  # non-uniform radii

Feature Detection ¶

Edge detection ¶

Edge detection filters generate images that have large value at the regions that signal change largely. You can consider them as scalar differentiation of images. Different edge detection filter used slightly different kernel but these kernels always take positive values on one side while take negative on the other.

img.edge_filter()  # Sobel filter by default
img.edge_filter(method="farid")  # Farid filter
img.edge_filter(method="scharr")  # Scharr filter
img.edge_filter(method="prewitt")  # Prewitt filter

Puncta detection ¶

Puncta detection filters are useful for automatic molecule detection with images taken by light or electron microscope. Note that images must be dark-background.

img.dog_filter()  # DoG (Difference of Gaussian)
img.doh_filter()  # DoH (Difference of Hessian)
img.log_filter()  # Log (Laplacian of Gaussian)

Filament detection ¶

A “filament” can be defined by 2nd derivative: convex in one direction and flat in the perpendicular direction. This trick can be achieved by inspecting the Hessian of an image.

hessian_eigval is composed of two steps. First, apply Gaussain filter to the image. Then, eigenvalues of Hessian are calculated. That’s why it has sigma argument.

vals = img.hessian_eigval()
vals = img.hessian_eigval(sigma=2.0)

The returned array has a new axis named "base", which corresponds to each spatial axis.

ImgArray of
      name      : random
     shape      : 3(base), 5(t), 32(z), 64(y), 64(x)
  label shape   : No label
     dtype      : float32
     source     : None
     scale      : ScaleView(base=1.0, t=1.0, z=1.0, y=1.0, x=1.0)