MATLAB: How can i remove the white object from binary image

binary imageimage processing

I have a image with my needed object (green) and the noise (white). I want to delete the noise but have no idea for that. anyone can help

Best Answer

You can use regionprops to compute circularity, eccentricity, or use major axis and minor axis lengths to find required shape of blob.

I used major and minor axis:

img = logical(rgb2gray(imread('green.png')));
dilate_img = imdilate(img,strel('disk',2));
BW = bwlabel(dilate_img);
blobprops = regionprops(BW, 'MajorAxisLength','MinorAxisLength', 'Eccentricity');
allMajorAxisLength = [blobprops.MajorAxisLength];
allMinorAxisLength = [blobprops.MinorAxisLength];
allEccentricity = [blobprops.Eccentricity];
MajorbyMinor = allMajorAxisLength./allMinorAxisLength;
[B,I] = sort(MajorbyMinor);
BW2 = ismember(BW,I(1));
% Using eccentricity
% [B,I] = sort(allEccentricity); % eccentricity = 0: circle, = 1: line
% BW2 = ismember(BW,I(1));
figure, 
subplot(131), imshow(img), title('Original');
subplot(132), imshow(BW), title('Label');
subplot(133), imshow(BW2), title('desired');

Related Solutions

MATLAB: How to make binary image more rounded, closer to ellipsoid shape

http://www.mathworks.com/matlabcentral/fileexchange/289-ellipse-m

MATLAB: Remove specific objects from an image

Following similar logic to this answer, we'll use regionprops to get the length and width of the bounding box rectangles. Then we can compute the length:width ratio, set a threshold, and determine which boxes are greater than that threshold.

Then you can plot accepted objects with a red rectangle and rejected objects with a yellow rectangle.

See below for a second method.

% Read image as RGB
I = imread('bwimage.png'); %including the pull path is better
% Convert to binary
BW = imbinarize(rgb2gray(I));
% Get rid of white border
BWConv = bwconvhull(~BW);
BW = BW & BWConv;
% get region centers and major axis, and orientation

stats = regionprops('table',BW,'BoundingBox');
% Compute length/width ratio

stats.LenWdRatio = stats.BoundingBox(:,3) ./ stats.BoundingBox(:,4);
% Set threshold and determine which objects
% are greater than the threshold. 
thresh = 2.0; 
stats.isGreater = stats.LenWdRatio > thresh; 
% Show bounding box for objects that met the threshold
figure()
h = imshow(BW); 
axis on
hold on
% Plot red rectangles around accepted objects

arrayfun(@(i)rectangle('Position',stats.BoundingBox(i,:),'EdgeColor','r'), find(stats.isGreater)); 
% Plot yellow rectangles around rejected objects

arrayfun(@(i)rectangle('Position',stats.BoundingBox(i,:),'EdgeColor','y'), find(~stats.isGreater));

Second method: using major:minor axis length

Instead of using the horizontal and vertical extent of the bounding box, you could use the ratio of the MajorAxisLength to the MinorAxisLength. Here's an example why this method might be better. Suppose you have an long object that stretchs along a diagonal with slope=1. The width and height of the bounding box will be equal even though it's a very long and narrow object. If you used the ratio of the Major:Minor axis lengths, the object will be detected as long and narrow even through the bounding box is square.

To use this method, make the following changes.

% get region centers and major axis, and orientation
stats = regionprops('table',BW,'BoundingBox','MajorAxisLength','MinorAxisLength');
% Compute length/width ratio
stats.LenWdRatio = stats.MajorAxisLength ./ stats.MinorAxisLength;

[Update]

I forgot to show how to remove the objects.

When you call regionprop, add the SubarrayIdx property to the list.

stats = regionprops('table',BW,'BoundingBox','MajorAxisLength','MinorAxisLength','SubarrayIdx');

Then you can loop through each rejected object and replace its values in BW with false (black).

% Remove rejected objects (fill with black)
objRemoveIdx = find(~stats.isGreater); 
for i = find(~stats.isGreater).'
    BW(stats.SubarrayIdx{i,1},stats.SubarrayIdx{i,2}) = false; 
end
% Show same figure with objects removed (yellow rectangles show where they were)
figure()
h = imshow(BW);
axis on
hold on
% Plot red rectangles around accepted objects
arrayfun(@(i)rectangle('Position',stats.BoundingBox(i,:),'EdgeColor','r'), find(stats.isGreater));
% Plot yellow rectangles around rejected objects
arrayfun(@(i)rectangle('Position',stats.BoundingBox(i,:),'EdgeColor','y'), find(~stats.isGreater));

Best Answer

Related Solutions

MATLAB: How to make binary image more rounded, closer to ellipsoid shape

MATLAB: Remove specific objects from an image

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