FeatureAdaptiveThresholding

Thresholds an image depending on two grayscale measurements.

Access to parameter description

This algorithm computes the thresholds of a grayscale image, given a label image corresponding to the pre-segmentation of the original image. Instead of giving two fixed values for the thresholding, two representative measurements are provided to the algorithm (for example, the 10th and the 90th percentile of the histogram, native measurements HistogramQuantile10 and HistogramQuantile90).
The minimum and maximum thresholding values are dynamically computed for each label and applied to give an adaptive thresholding on each of them.
For instance, the label image can be a grid of contiguous squares of different intensity to ensure a thresholding locally adapted to each square.

See also

Function Syntax

This function returns outputBinaryImage.

// Function prototype
std::shared_ptr< iolink::ImageView >
featureAdaptiveThresholding( std::shared_ptr< iolink::ImageView > inputLabelImage,
                             std::shared_ptr< iolink::ImageView > inputGrayImage,
                             std::string measurementLow,
                             std::string measurementHigh,
                             std::shared_ptr< iolink::ImageView > outputBinaryImage = nullptr );

This function returns outputBinaryImage.

// Function prototype.
feature_adaptive_thresholding(input_label_image: idt.ImageType,
                              input_gray_image: idt.ImageType,
                              measurement_low: str = "",
                              measurement_high: str = "",
                              output_binary_image: idt.ImageType = None) -> idt.ImageType

This function returns outputBinaryImage.

// Function prototype.
public static IOLink.ImageView
FeatureAdaptiveThresholding( IOLink.ImageView inputLabelImage,
                             IOLink.ImageView inputGrayImage,
                             string measurementLow = "",
                             string measurementHigh = "",
                             IOLink.ImageView outputBinaryImage = null );

Class Syntax

// Command constructor.
FeatureAdaptiveThresholding();

/// Gets the inputLabelImage parameter.
/// The input label image.
std::shared_ptr< iolink::ImageView > inputLabelImage() const;
/// Sets the inputLabelImage parameter.
/// The input label image.
void setInputLabelImage( std::shared_ptr< iolink::ImageView > inputLabelImage );

/// Gets the inputGrayImage parameter.
/// The input grayscale image. This image must have same dimensions as the input label image.
std::shared_ptr< iolink::ImageView > inputGrayImage() const;
/// Sets the inputGrayImage parameter.
/// The input grayscale image. This image must have same dimensions as the input label image.
void setInputGrayImage( std::shared_ptr< iolink::ImageView > inputGrayImage );

/// Gets the measurementLow parameter.
/// The measurement defining the lower threshold.
std::string measurementLow() const;
/// Sets the measurementLow parameter.
/// The measurement defining the lower threshold.
void setMeasurementLow( const std::string& measurementLow );

/// Gets the measurementHigh parameter.
/// The measurement defining the higher threshold.
std::string measurementHigh() const;
/// Sets the measurementHigh parameter.
/// The measurement defining the higher threshold.
void setMeasurementHigh( const std::string& measurementHigh );

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


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

# Property of the inputLabelImage parameter.
FeatureAdaptiveThresholding.input_label_image
# Property of the inputGrayImage parameter.
FeatureAdaptiveThresholding.input_gray_image
# Property of the measurementLow parameter.
FeatureAdaptiveThresholding.measurement_low
# Property of the measurementHigh parameter.
FeatureAdaptiveThresholding.measurement_high
# Property of the outputBinaryImage parameter.
FeatureAdaptiveThresholding.output_binary_image

// Method to launch the command.
execute()

// Command constructor.
FeatureAdaptiveThresholding()

// Property of the inputLabelImage parameter.
FeatureAdaptiveThresholding.inputLabelImage
// Property of the inputGrayImage parameter.
FeatureAdaptiveThresholding.inputGrayImage
// Property of the measurementLow parameter.
FeatureAdaptiveThresholding.measurementLow
// Property of the measurementHigh parameter.
FeatureAdaptiveThresholding.measurementHigh
// Property of the outputBinaryImage parameter.
FeatureAdaptiveThresholding.outputBinaryImage

// Method to launch the command.
Execute()

Parameters

	Description	Type	Supported Values	Default Value
inputLabelImage	The input label image.	Image	Binary or Label	nullptr
inputGrayImage	The input grayscale image. This image must have same dimensions as the input label image.	Image	Grayscale	nullptr
measurementLow	The measurement defining the lower threshold.	Measurement		""
measurementHigh	The measurement defining the higher threshold.	Measurement		""
outputBinaryImage	The output binary image. Its dimensions are forced to the same values as the input.	Image		nullptr

	Description	Type	Supported Values	Default Value
input_label_image	The input label image.	image	Binary or Label	None
input_gray_image	The input grayscale image. This image must have same dimensions as the input label image.	image	Grayscale	None
measurement_low	The measurement defining the lower threshold.	measurement		""
measurement_high	The measurement defining the higher threshold.	measurement		""
output_binary_image	The output binary image. Its dimensions are forced to the same values as the input.	image		None

	Description	Type	Supported Values	Default Value
inputLabelImage	The input label image.	Image	Binary or Label	null
inputGrayImage	The input grayscale image. This image must have same dimensions as the input label image.	Image	Grayscale	null
measurementLow	The measurement defining the lower threshold.	Measurement		""
measurementHigh	The measurement defining the higher threshold.	Measurement		""
outputBinaryImage	The output binary image. Its dimensions are forced to the same values as the input.	Image		null

Object Examples

auto polystyrene_sep_label = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene_sep_label.vip" );
auto polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

FeatureAdaptiveThresholding featureAdaptiveThresholdingAlgo;
featureAdaptiveThresholdingAlgo.setInputLabelImage( polystyrene_sep_label );
featureAdaptiveThresholdingAlgo.setInputGrayImage( polystyrene );
featureAdaptiveThresholdingAlgo.setMeasurementLow( "HistogramQuantile10" );
featureAdaptiveThresholdingAlgo.setMeasurementHigh( "HistogramQuantile90" );
featureAdaptiveThresholdingAlgo.execute();

std::cout << "outputBinaryImage:" << featureAdaptiveThresholdingAlgo.outputBinaryImage()->toString();

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

feature_adaptive_thresholding_algo = imagedev.FeatureAdaptiveThresholding()
feature_adaptive_thresholding_algo.input_label_image = polystyrene_sep_label
feature_adaptive_thresholding_algo.input_gray_image = polystyrene
feature_adaptive_thresholding_algo.measurement_low = "HistogramQuantile10"
feature_adaptive_thresholding_algo.measurement_high = "HistogramQuantile90"
feature_adaptive_thresholding_algo.execute()

print("output_binary_image:", str(feature_adaptive_thresholding_algo.output_binary_image))

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

FeatureAdaptiveThresholding featureAdaptiveThresholdingAlgo = new FeatureAdaptiveThresholding
{
    inputLabelImage = polystyrene_sep_label,
    inputGrayImage = polystyrene,
    measurementLow = "HistogramQuantile10",
    measurementHigh = "HistogramQuantile90"
};
featureAdaptiveThresholdingAlgo.Execute();

Console.WriteLine( "outputBinaryImage:" + featureAdaptiveThresholdingAlgo.outputBinaryImage.ToString() );

Function Examples

auto polystyrene_sep_label = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene_sep_label.vip" );
auto polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

auto result = featureAdaptiveThresholding( polystyrene_sep_label, polystyrene, "HistogramQuantile10", "HistogramQuantile90" );

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

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

result = imagedev.feature_adaptive_thresholding(polystyrene_sep_label, polystyrene, "HistogramQuantile10", "HistogramQuantile90")

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

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

IOLink.ImageView result = Processing.FeatureAdaptiveThresholding( polystyrene_sep_label, polystyrene, "HistogramQuantile10", "HistogramQuantile90" );

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

FeatureAdaptiveThresholding

- Function Syntax

+ Class Syntax

Parameters

Object Examples

Function Examples

Function Syntax

Class Syntax