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imgaussfilt3

3-D Gaussian filtering of 3-D images

Description

B = imgaussfilt3(A) filters 3-D image A with a 3-D Gaussian smoothing kernel with standard deviation of 0.5, and returns the filtered image in B.

example

B = imgaussfilt3(A,sigma) filters 3-D image A with a 3-D Gaussian smoothing kernel with standard deviation specified by sigma.

B = imgaussfilt3(___,Name,Value) uses name-value pair arguments to control aspects of the filtering.

Examples

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Load MRI data and display it.

vol = load('mri');
figure
montage(vol.D)
title('Original image volume')

Figure contains an axes object. The hidden axes object with title Original image volume contains an object of type image.

Smooth the image with a 3-D Gaussian filter.

siz = vol.siz;
vol = squeeze(vol.D);   
sigma = 2;
 
volSmooth = imgaussfilt3(vol, sigma);
  
figure
montage(reshape(volSmooth,siz(1),siz(2),1,siz(3)))
title('Gaussian filtered image volume')

Figure contains an axes object. The hidden axes object with title Gaussian filtered image volume contains an object of type image.

Input Arguments

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Image to be filtered, specified as a 3-D numeric array.

Data Types: single | double | int8 | uint8 | int16 | uint16 | int32 | uint32

Standard deviation of the Gaussian distribution, specified as positive number or a 3-element vector of positive numbers. If sigma is a scalar, then imgaussfilt3 uses a cubic Gaussian kernel.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: volSmooth = imgaussfilt3(vol,sigma,"padding","circular");

Size of the Gaussian filter, specified as a scalar or 3-element vector of positive, odd, integers. If you specify a scalar, then imgaussfilt3 uses a cubic filter. The default filter size is 2*ceil(2*sigma)+1.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Image padding, specified as one of the following.

ValueDescription
numeric scalarPad image with elements of constant value.
"circular"

Pad with circular repetition of elements within the dimension.

"replicate"

Pad by repeating border elements of array.

"symmetric"

Pad image with mirror reflections of itself.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string

Domain in which to perform filtering, specified as one of the following values.

Filter Domain Description
"auto"Perform convolution in the spatial or frequency domain, based on internal heuristics.
"frequency"Perform convolution in the frequency domain.
"spatial"Perform convolution in the spatial domain.

Data Types: char | string

Output Arguments

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Filtered image, returned as a numeric array of the same class and size as input image.

Tips

  • If image A contains Infs or NaNs, then the behavior of imgaussfilt3 for frequency domain filtering is undefined. This can happen if you set the FilterDomain argument to "frequency" or if you set it to "auto" and imgaussfilt3 uses frequency domain filtering. To restrict the propagation of Infs and NaNs in the output in a manner similar to imfilter, consider setting the FilterDomain argument to "spatial".

  • If you set the FilterDomain argument to "auto", then imgaussfilt3 uses an internal heuristic to determine whether spatial or frequency domain filtering is faster. This heuristic is machine-dependent and may vary for different configurations. For optimal performance, try both options, "spatial" and "frequency", to determine the best filtering domain for your image and kernel size.

  • If you do not specify the Padding argument, then imgaussfilt3 uses "replicate" padding by default, which is different from the default used by imfilter.

Extended Capabilities

Version History

Introduced in R2015a

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