medfilt3

1D, 2D and 3D median filtering

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Syntax
Description
2-D median filtering
3-D median filtering
See also
About the author

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medfilt3.m

Syntax

   B = medfilt3(A,[...])
   B = medfilt3(A)
   B = medfilt3(A,...,padopt)

Description

B = medfilt3(A,[m n p]) performs median filtering of the 3-D array A. Each output pixel contains the median value in the m by n by p neighborhood around the corresponding pixel in the input array.

B = medfilt3(A,[m n]) performs median filtering of the matrix A. Each output pixel contains the median value in the m by n neighborhood around the corresponding pixel.

B = medfilt3(A,m) performs median filtering of the vector A. Each output pixel contains the median value in the m neighborhood around the corresponding pixel.

B = medfilt3(A) performs median filtering using a 3 or 3x3 or 3x3x3 neighborhood according to the size of A.

B = medfilt3(A,...,padopt) pads array A using padopt option before filtering. String values for padopt (default = 'replicate'):

'circular': Pads with circular repetition of elements.
'replicate': Repeats border elements of A. (default)
'symmetric': Pads array with mirror reflections of itself.
If padopt is a scalar, A is padded with this scalar.

Class Support: Input array can be numeric or logical.

Notes:

m, n and/or p must be odd integers. If not, they are incremented by 1.
If nanmedian exists (Statistics Toolbox is required), then medfilt3 treats NaNs as missing values.
If you work with very large 3D arrays, an "Out of memory" error may appear. The chunk factor (chunkfactor, default value = 1) must be increased to reduce the size of the chunks. This will imply more iterations whose number is directly proportional to chunkfactor. Use the following syntax: medfilt3(A,[...],padopt,chunkfactor).

2-D median filtering

Let us read an image and make it noisy with salt-and-pepper noise...

I = imread('eight.tif');
I(rand(size(I))<0.01) = 255;
I(rand(size(I))<0.01) = 0;
imshow(I), title('Noisy image')

And let us reconstruct the image using a 2-D median filter:

J = medfilt3(I);
imshow(J), title('Filtered image')

3-D median filtering

This examples uses a median filter with a 5x5x5 neighborhood.

First create a 3-D flow data and add some white noise:

[x,y,z,V] = flow(50);
noisyV = V + 0.1*double(rand(size(V))>0.95);

Filter the data with a 3-D median filter:

filteredV = medfilt3(noisyV);

And display the result:

subplot(121)
hpatch = patch(isosurface(x,y,z,noisyV,0));
isonormals(x,y,z,noisyV,hpatch)
set(hpatch,'FaceColor','red','EdgeColor','none')
daspect([1,4,4]), view([-65,20]), axis tight off
camlight left; lighting phong
subplot(122)
hpatch = patch(isosurface(x,y,z,filteredV,0));
isonormals(x,y,z,filteredV,hpatch)
set(hpatch,'FaceColor','red','EdgeColor','none')
daspect([1,4,4]), view([-65,20]), axis tight off
camlight left; lighting phong

About the author

Damien Garcia, Eng., Ph.D.
Assistant professor, Department of radiology
CRCHUM, University of Montreal Hospital
Montreal, QC, Canada
Damien.Garcia.REMOVE-THIS@BiomeCardio.com