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:

section Image Analysis
Native analysis measurements

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:

An object which measurement falls between 2 sieve values is assigned to a corresponding label. For instance, all objects between value-1 and value-2 are assigned to the label 2.
Objects which measurement is less than the first sieve value are assigned to 1.
Objects which measurement is greater than the last sieve value are assigned to the greatest label value (the number of sieve bounds + 1).
If N sieve values have been provided as input, the output image can contain up to N+1 different labels.

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 = nullptr );

This function returns outputLabelImage.

// Function prototype.
sieve_labeling(input_object_image: idt.ImageType,
               input_intensity_image: idt.ImageType,
               sieve_array: Union[Sequence[int], Sequence[float]] = [10, 100, 1000, 10000],
               measurement: str = "",
               output_label_image: idt.ImageType = None) -> idt.ImageType

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

// Command constructor.
SieveLabeling();

/// Gets the inputObjectImage parameter.
/// The input binary or label image.
std::shared_ptr< iolink::ImageView > inputObjectImage() const;
/// Sets the inputObjectImage parameter.
/// The input binary or label image.
void setInputObjectImage( std::shared_ptr< iolink::ImageView > inputObjectImage );

/// Gets the inputIntensityImage parameter.
/// 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.
std::shared_ptr< iolink::ImageView > inputIntensityImage() const;
/// Sets the inputIntensityImage parameter.
/// 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.
void setInputIntensityImage( std::shared_ptr< iolink::ImageView > inputIntensityImage );

/// Gets the sieveArray parameter.
/// The array defining the bounds values of the sieve to apply.
std::vector<double> sieveArray() const;
/// Sets the sieveArray parameter.
/// The array defining the bounds values of the sieve to apply.
void setSieveArray( const std::vector<double>& sieveArray );

/// Gets the measurement parameter.
/// The native measurement on which the sieve is applied.
std::string measurement() const;
/// Sets the measurement parameter.
/// The native measurement on which the sieve is applied.
void setMeasurement( const std::string& measurement );

/// Gets the outputLabelImage parameter.
/// The output label image. Its dimensions are forced to the same values as the input.
std::shared_ptr< iolink::ImageView > outputLabelImage() const;
/// Sets the outputLabelImage parameter.
/// The output label image. Its dimensions are forced to the same values as the input.
void setOutputLabelImage( std::shared_ptr< iolink::ImageView > outputLabelImage );


// Method to launch the command.
void execute();

# Property of the inputObjectImage parameter.
SieveLabeling.input_object_image
# Property of the inputIntensityImage parameter.
SieveLabeling.input_intensity_image
# Property of the sieveArray parameter.
SieveLabeling.sieve_array
# Property of the measurement parameter.
SieveLabeling.measurement
# Property of the outputLabelImage parameter.
SieveLabeling.output_label_image

// Method to launch the command.
execute()

// Command constructor.
SieveLabeling()

// Property of the inputObjectImage parameter.
SieveLabeling.inputObjectImage
// Property of the inputIntensityImage parameter.
SieveLabeling.inputIntensityImage
// Property of the sieveArray parameter.
SieveLabeling.sieveArray
// Property of the measurement parameter.
SieveLabeling.measurement
// Property of the outputLabelImage parameter.
SieveLabeling.outputLabelImage

// Method to launch the command.
Execute()

Parameters

	Description	Type	Supported Values	Default Value
inputObjectImage	The input binary or label image.	Image	Binary or Label	nullptr
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
sieveArray	The array defining the bounds values of the sieve to apply.	Sieve		{10, 100, 1000, 10000}
measurement	The native measurement on which the sieve is applied.	Measurement		""
outputLabelImage	The output label image. Its dimensions are forced to the same values as the input.	Image		nullptr

	Description	Type	Supported Values	Default Value
input_object_image	The input binary or label image.	image	Binary or Label	None
input_intensity_image	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	None
sieve_array	The array defining the bounds values of the sieve to apply.	sieve		[10, 100, 1000, 10000]
measurement	The native measurement on which the sieve is applied.	measurement		""
output_label_image	The output label image. Its dimensions are forced to the same values as the input.	image		None

	Description	Type	Supported Values	Default Value
inputObjectImage	The input binary or label image.	Image	Binary or Label	null
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	null
sieveArray	The array defining the bounds values of the sieve to apply.	Sieve		{10, 100, 1000, 10000}
measurement	The native measurement on which the sieve is applied.	Measurement		""
outputLabelImage	The output label image. Its dimensions are forced to the same values as the input.	Image		null

Object Examples

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

SieveLabeling

- Function Syntax

+ Class Syntax

Parameters

Object Examples

Function Examples

Function Syntax

Class Syntax