url = “https://www.mathworks.com/matlabcentral/solutions/uploaded_files/880740/picture.jpeg”;
imshow(A)
Picture credit score: Aktham Shoukry. Used with permission.
Right here is an instance of what Aktham meant by “channel.”
This submit reveals one strategy to accomplish the duty, utilizing erosion, morphological reconstruction, and a few picture arithmetic.
The channels are comparatively skinny in a single dimension, both horizontally or vertically. So, a morphological erosion may be constructed that can preserve a portion of every grain, whereas utterly eliminating the channels.
First, a little bit of preprocessing.
imshow(B)
The channels are about 10 pixels thick, whereas the square-ish “grains” are about 50-by-50. Let’s erode with a sq. structuring aspect with a dimension that is in between.
C = imerode(B,ones(25,25));
imshow(C)
Subsequent, use morphological reconstruction to revive the unique grain shapes.
imshow(D)
Complement the picture to make the grains darkish once more.
imshow(E)
Subtracting the unique grayscale picture from E will get us very shut.
imshow(F)
imshow(G)
A closing cleanup of the small objects, utilizing both bwareafilt or bwareaopen, offers us our end result.
imshow(H)
The perform imoverlay offers us a pleasant visualization of the end result. (You can additionally use imfuse.)
Okay = imoverlay(Ag,H,[.8500 .3250 .0980]);
imshow(Okay)
Word, nevertheless, that our end result is not fairly excellent. There are partially cropped-off grains alongside the highest border which have been recognized as channels. Usually, I’d suggest utilizing imclearborder as a preprocessing step, however on this case, imclearborder eliminates too most of the desired channels.
I’m open to options about the best way to refine this technique. Go away a remark and let me know what you assume.
