ImageDev

SnnFilter3d

Performs an edge-preserving smoothing of a three-dimensional image with the Symmetric Nearest Neighbor filter.

Access to parameter description

For an introduction to image filters: Images Filtering.

This algorithm implements the Symmetric Nearest Neighbor filter (SNN), which is an average filter weighted by the structural symmetry of a rectangular neighborhood window. It computes a local mean around a central voxel $x$, considering for each neighbor voxel $y$, having a gray level $I(y)$, the value given by:
$$ I'(y)=\left\{\begin{array}{ll} I(y) & ~\mbox{if $|I(x)-I(y)| < |I(x)-I(y_s)|$} \\ I(y_s) & ~\mbox{if $|I(x)-I(y)| > |I(x)-I(y_s)|$} \\ I(x) & ~\mbox{otherwise} \end{array}\right. $$ Where $y_s$ is the neighbor voxel symmetric to $y$ with respect to $x$.

The output value of the voxel $x$ is then given by: $$ O(x)=\mu[I'(y)] $$ Where $\mu$ is the mean value of the local window.

See also

Function Syntax

This function returns the outputImage output parameter. 
// Function prototype.
std::shared_ptr< iolink::ImageView >
snnFilter3d( std::shared_ptr< iolink::ImageView > inputImage,
             int32_t kernelSizeX,
             int32_t kernelSizeY,
             int32_t kernelSizeZ,
             std::shared_ptr< iolink::ImageView > outputImage = NULL );
This function returns the outputImage output parameter. 
// Function prototype.
snn_filter_3d( input_image,
               kernel_size_x = 3,
               kernel_size_y = 3,
               kernel_size_z = 3,
               output_image = None )
This function returns the outputImage output parameter. 
// Function prototype.
public static IOLink.ImageView
SnnFilter3d( IOLink.ImageView inputImage,
             Int32 kernelSizeX = 3,
             Int32 kernelSizeY = 3,
             Int32 kernelSizeZ = 3,
             IOLink.ImageView outputImage = null );

Class Syntax

Parameters

Class Name SnnFilter3d

Parameter Name Description Type Supported Values Default Value
input
inputImage
The input image. Image Binary, Label, Grayscale or Multispectral nullptr
input
kernelSizeX
The horizontal kernel size in voxels (odd value). Int32 [3, 100] 3
input
kernelSizeY
The vertical kernel size in voxels (odd value). Int32 [3, 100] 3
input
kernelSizeZ
The depth kernel size in voxels (odd value). Int32 [3, 100] 3
output
outputImage
The output image. Its dimensions, type, and calibration are forced to the same values as the input. Image nullptr

Object Examples

auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );

SnnFilter3d snnFilter3dAlgo;
snnFilter3dAlgo.setInputImage( foam );
snnFilter3dAlgo.setKernelSizeX( 3 );
snnFilter3dAlgo.setKernelSizeY( 3 );
snnFilter3dAlgo.setKernelSizeZ( 3 );
snnFilter3dAlgo.execute();

std::cout << "outputImage:" << snnFilter3dAlgo.outputImage()->toString();

foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))

snn_filter_3d_algo = imagedev.SnnFilter3d()
snn_filter_3d_algo.input_image = foam
snn_filter_3d_algo.kernel_size_x = 3
snn_filter_3d_algo.kernel_size_y = 3
snn_filter_3d_algo.kernel_size_z = 3
snn_filter_3d_algo.execute()

print( "output_image:", str( snn_filter_3d_algo.output_image ) );

ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );

SnnFilter3d snnFilter3dAlgo = new SnnFilter3d
{
    inputImage = foam,
    kernelSizeX = 3,
    kernelSizeY = 3,
    kernelSizeZ = 3
};
snnFilter3dAlgo.Execute();

Console.WriteLine( "outputImage:" + snnFilter3dAlgo.outputImage.ToString() );

Function Examples

auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );

auto result = snnFilter3d( foam, 3, 3, 3 );

std::cout << "outputImage:" << result->toString();

foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))

result = imagedev.snn_filter_3d( foam, 3, 3, 3 )

print( "output_image:", str( result ) );

ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );

IOLink.ImageView result = Processing.SnnFilter3d( foam, 3, 3, 3 );

Console.WriteLine( "outputImage:" + result.ToString() );