bwareaopen
result =bwareaopen(binary_img, min_area)(alias ofremove_small_blobs)
Input: binary_img, min_area · Output: result
What it is
binary_img is a binary Image; min_area is a number, the minimum pixel count a blob must have to survive. Labels internally (8-connectivity, matching MATLAB's default) and zeroes out every blob whose pixel count is below min_area.
Overloads
One call shape — this function is not overloaded.
| Code | Input | Output | Description |
result = bwareaopen(binary_img, min_area) (alias of remove_small_blobs) | binary_img Imagebinary_img is a binary Image.min_area numbermin_area is a number, the minimum pixel count a blob must have to survive. | result Image | Returns a new binary Image, same dimensions as binary_img, with small blobs removed. |
Examples
From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.
# Three bright discs on a dark field, written out and read back. The blob
# count printed below is then checkable against an image the reader can
# see being built, rather than asserted about a file nobody has.
n = 60
field = zeros(n, n)
centres = [15, 15; 42, 20; 30, 45]
for i in 0 to n - 1
for j in 0 to n - 1
for k in 0 to 2
dy = i - centres[k, 0]
dx = j - centres[k, 1]
if dy * dy + dx * dx < 64
field[i, j] = 230
end if
end for
end for
end for
save_image("cells.bmp", image_from_matrix(field))
img = load_image("cells.bmp")
level = otsu_threshold(img)
mask = threshold(img, level)
clean = bwareaopen(mask, 20) # drop specks smaller than 20 px
imshow(clean)
labeled = bwlabel(clean)
print(labeled.count) # number of detected blobs
stats = regionprops(labeled)
for i = 1 to labeled.count
r = stats[i - 1]
print(r.label, r.area, r.centroid_x, r.centroid_y)
end for
3 1 193 15 15 2 193 45 30 3 193 20 42
from images
How it works
remove_small_blobs(binary_img, min_area) (alias bwareaopen) — labels internally (no separate label_blobs call needed), zeroes out every blob whose pixel count is below min_area, and returns a cleaned binary image in the same 0/255 mask convention (always 8-connectivity, matching MATLAB's own bwareaopen default).
From the note above this builtin in the interpreter, where the reasoning was written down as it was built.