specklefilt

Import an ultrasound image into the workspace.

I = imread("heartUltrasoundImage.png"); 

Filter the image using the speckle-reducing anisotropic diffusion algorithm with default parameters.

J1 = specklefilt(I);

You can increase the smoothing the filter performs on the image by increasing the degree of smoothing of the algorithm. Specify a degree of smoothing of 0.6, increased from the default value of 0.2.

J2 = specklefilt(I,DegreeOfSmoothing=0.6);

You can also increase the smoothing by increasing the number of iterations performed by the algorithm. Specify for the algorithm to perform 50 iterations, increased from the default value of 30.

J3 = specklefilt(I,NumIterations=50);

Filter the image using the SRAD algorithm with both an increased degree of smoothing and greater number of iterations. Set the degree of smoothing to 0.6 and the number of iterations to 50.

J4 = specklefilt(I,DegreeOfSmoothing=0.6,NumIterations=50);

Display the original image alongside the denoised images, and inspect the different smoothing levels.

figure
montage({I J1 J2 J3 J4})

Import an ultrasound image into the workspace.

I = imread("heartUltrasoundImage.png"); 

Corrupt the image with multiplicative speckle drawn from a uniform distribution.

Inoise = imnoise(I,"speckle");

Filter the corrupted image to remove speckle. Display the corrupted and denoised images.

J = specklefilt(Inoise,DegreeOfSmoothing=0.6,NumIterations=50);
figure
montage({Inoise J})

Compute the structural similarity of the denoised image J with the original image I.

disp(ssim(I,J))

    0.9429

Corrupt the image with multiplicative speckle drawn from a Rayleigh distribution.

I = im2double(I);
Inoise = I.*raylrnd(1,size(I));

Filter the corrupted image to remove speckle. Display the corrupted and denoised images.

J = specklefilt(Inoise,DegreeOfSmoothing=0.6,NumIterations=50);
figure
montage({Inoise J})

Compute the structural similarity between the denoised image J and the original image I.

disp(ssim(I,J))

    0.7781

Import an ultrasound image sequence into the workspace.

I = dicomread("heartUltrasoundSequence.dcm");
[h,w,c,d] = size(I);

View the original image sequence.

figure
montage(I)
title("Original Image Sequence")

Convert each image in the sequence to grayscale. Filter the grayscale images.

J = zeros(h,w,d);
for i = 1:d
    Ii = im2double(im2gray(I(:,:,:,i)));
    J(:,:,i) = specklefilt(Ii,DegreeOfSmoothing=0.6,NumIterations=50);
end

View the denoised image sequence.

figure
montage(J)
title("Denoised Image Sequence")