I figured I would post the answer since I figured it out. If I add the line binaryImage = imcomplement(binaryImage) in it inverts what is black and white and that works for this. It apparently wanted to have the objects be white and not black (?) which is a bit confusing to me, but it works now. Full code: image = imread('Gaussian.jpg'); % load in your image binaryImage = im2bw(image, 0.5); % converts image to binary file binaryImage = imcomplement(binaryImage) % inverts black and white BW2 = bwareaopen(binaryImage, 150); % removes objects comprised of < 150 pixels imshowpair(binaryImage, BW2, 'montage') % display both images to see if spots are removed

You have 2 connected blobs. The one on the left is 8 pixels big (area of 8 pixels). The blob on the right is 3 pixels. When you called bwareaopen, it got rid of blobs less than 4 pixels. Since the blob with an area of 3 is less than 4, it was removed. Does that explain it? It has nothing to do with connectivity here because all your blobs are 4-connected. Now if you had an extra pixel diagonally connected to the blob on the left, like this: A = 0 1 0 0 1 0 0 1 1 0 1 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 Now there are 8 eight connected blobs, but 3 blobs if you consider them as 4 connected. The pixel at row 4 column 4 is 8-connected to the blob on the left, but not 4 connected. It would be removed with bwareaopen(A, 4, 4) but not with bwareaopen(A, 4, 8) because in the second case it's connected while in the first case it's not connected.

You are mixing 2 of the variables. in the documentation we can see the following:

BW2 = bwareaopen(BW, P)
BW2 = bwareaopen(BW, P, conn)

Where you have P and conn variables.

conn is the neighbourhood variable. With this you chose with values you understand as "neigbour". See example:

conn=4;

- X -
X 0 X
- X - 

conn=8

X X X
X 0 X
X X X

Higher numbers are for higher dimensions. but the P variable is the variable to choose what is "small". The function bwareaopen will delete white areas smaller than P pixels.

In the example in the documentation you can see:

    BW = imread('text.png');
    BW2 = bwareaopen(BW, 50);
    imshow(BW);

But that "50" is the P variable, not conn. as conn is not defined it will be set as default, in this case 8.

Take a look at the cvBlobsLib, it has functions to do what you want. In fact, the code example on the front page of that link does exactly what you want, I think. Essentially, you can use CBlobResult to perform connected-component labeling on your binary image, and then call Filter to exclude blobs according to your criteria.

Make sure that you call |bwareaopen| on a binary image. I executed the following code on the attached JPEG image and it worked: % Read original image I = imread('inputimg.jpg'); figure(1); imshow(I) % Convert to binary image BW = im2bw(I, 0.5); figure(2); imshow(BW) % Do morphological opening to remove small connected components BW2 = bwareaopen(BW, 50); figure(3); imshow(BW2)

bwareaopen() removes white areas with size less than p. If you want to remvove the black holes inside your white areas you should use the function imfill() .

Edit: Alternativly, you could use the bwareaopen() function on the inverse image, i.e. on 1-Im

Given some image, for example (just to make a random image): I = conv2(randn(500),ones(10)) > 10; imshow(I); To keep only objects between, say, 30 pixels and 50 pixels in area, you can use the BWAREAOPEN command, like this: LB = 30; UB = 50; Iout = xor(bwareaopen(I,LB), bwareaopen(I,UB)); figure, imshow(Iout);

The following will remove any areas greater than 8000 pixels: M3 = M2 & ~bwareaopen(M2,8000); Another option would be to use regionprops() to get the area of every mask and then remove [stats.Area]>8000, but I think the above 1-line solution is cleaner.

Numpy/Scipy can do morphological operations just as well as Matlab can.

See scipy.ndimage.morphology, containing, among other things, binary_opening(), the equivalent of Matlab's bwareaopen().