ImageDev

SieveLabeling

Produces a new label image by assigning a same label to objects belonging to a same class of a user-defined measurement.

Access to parameter description

As an introduction:
This algorithm produces a new label image by grouping objects of the input label or binary image. The objects are then assigned to bins using a measurement and a sieve array. The output labels are set accordingly.
<b> Figure 1.</b> Label grouping based on a sieve set
Figure 1. Label grouping based on a sieve set

First, a user-defined measurement is computed for each object of the input image. Then, each object of the input image is assigned to an output label depending on the value of this measurement. The Figure 1 illustrates the label assignment procedure:


Figure 2. Sieve labeling applied on a binary image with the Area2d measurement.

See also

Function Syntax

This function returns outputLabelImage. 
// Function prototype
std::shared_ptr< iolink::ImageView >
sieveLabeling( std::shared_ptr< iolink::ImageView > inputObjectImage,
               std::shared_ptr< iolink::ImageView > inputIntensityImage,
               std::vector<double> sieveArray,
               std::string measurement,
               std::shared_ptr< iolink::ImageView > outputLabelImage = NULL );
This function returns outputLabelImage. 
// Function prototype.
sieve_labeling( input_object_image,
                input_intensity_image,
                sieve_array = [10, 100, 1000, 10000],
                measurement = "",
                output_label_image = None )
This function returns outputLabelImage. 
// Function prototype.
public static IOLink.ImageView
SieveLabeling( IOLink.ImageView inputObjectImage,
               IOLink.ImageView inputIntensityImage,
               double[] sieveArray = null,
               string measurement = "",
               IOLink.ImageView outputLabelImage = null );

Class Syntax

Parameters

Class Name SieveLabeling

Parameter Name Description Type Supported Values Default Value
input
inputObjectImage
The input binary or label image. Image Binary or Label nullptr
input
inputIntensityImage
The input grayscale image that contains the intensity information. It must have the same dimensions as the label input image. It is only used with measurements requiring an intensity image, and is ignored with other measurements. If it equals null, the label input image will be used as the intensity input image. Image Binary, Label, Grayscale or Multispectral nullptr
input
sieveArray
The array defining the bounds values of the sieve to apply. Sieve {10, 100, 1000, 10000}
input
measurement
The native measurement on which the sieve is applied. Measurement ""
output
outputLabelImage
The output label image. Its dimensions are forced to the same values as the input. Image nullptr

Object Examples

auto polystyrene_sep = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene_sep.vip" );
std::shared_ptr< iolink::ImageView > polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

SieveLabeling sieveLabelingAlgo;
sieveLabelingAlgo.setInputObjectImage( polystyrene_sep );
sieveLabelingAlgo.setInputIntensityImage( polystyrene );
sieveLabelingAlgo.setSieveArray( {1800, 2000, 2300, 2600, 3000} );
sieveLabelingAlgo.setMeasurement( "Area" );
sieveLabelingAlgo.execute();

std::cout << "outputLabelImage:" << sieveLabelingAlgo.outputLabelImage()->toString();

polystyrene_sep = imagedev.read_vip_image(imagedev_data.get_image_path("polystyrene_sep.vip"))
polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))

sieve_labeling_algo = imagedev.SieveLabeling()
sieve_labeling_algo.input_object_image = polystyrene_sep
sieve_labeling_algo.input_intensity_image = polystyrene
sieve_labeling_algo.sieve_array = [1800, 2000, 2300, 2600, 3000]
sieve_labeling_algo.measurement = "Area"
sieve_labeling_algo.execute()

print( "output_label_image:", str( sieve_labeling_algo.output_label_image ) )

ImageView polystyrene_sep = Data.ReadVipImage( @"Data/images/polystyrene_sep.vip" );
ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );

SieveLabeling sieveLabelingAlgo = new SieveLabeling
{
    inputObjectImage = polystyrene_sep,
    inputIntensityImage = polystyrene,
    sieveArray = new double[]{1800, 2000, 2300, 2600, 3000},
    measurement = "Area"
};
sieveLabelingAlgo.Execute();

Console.WriteLine( "outputLabelImage:" + sieveLabelingAlgo.outputLabelImage.ToString() );

Function Examples

auto polystyrene_sep = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene_sep.vip" );
std::shared_ptr< iolink::ImageView > polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

auto result = sieveLabeling( polystyrene_sep, polystyrene, {1800, 2000, 2300, 2600, 3000}, "Area" );

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

polystyrene_sep = imagedev.read_vip_image(imagedev_data.get_image_path("polystyrene_sep.vip"))
polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))

result = imagedev.sieve_labeling( polystyrene_sep, polystyrene, [1800, 2000, 2300, 2600, 3000], "Area" )

print( "output_label_image:", str( result ) )

ImageView polystyrene_sep = Data.ReadVipImage( @"Data/images/polystyrene_sep.vip" );
ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );

IOLink.ImageView result = Processing.SieveLabeling( polystyrene_sep, polystyrene, new double[]{1800, 2000, 2300, 2600, 3000}, "Area" );

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