Math and statistics » Statistics module

Image sample statistics.

Contents

See also Projection operators.

Functions

auto dip::Count(dip::Image const& in, dip::Image const& mask = {}) -> dip::uint
Counts the number of non-zero pixels in a scalar image.
auto dip::MaximumPixel(dip::Image const& in, dip::Image const& mask = {}, dip::String const& positionFlag = S::FIRST) -> dip::UnsignedArray
Returns the coordinates of the maximum pixel in the image.
auto dip::MinimumPixel(dip::Image const& in, dip::Image const& mask = {}, dip::String const& positionFlag = S::FIRST) -> dip::UnsignedArray
Returns the coordinates of the minimum pixel in the image.
void dip::CumulativeSum(dip::Image const& in, dip::Image const& mask, dip::Image& out, dip::BooleanArray const& process = {})
Calculates the cumulative sum of the pixel values over all those dimensions which are specified by process.
auto dip::MaximumAndMinimum(dip::Image const& in, dip::Image const& mask = {}) -> dip::MinMaxAccumulator
Finds the largest and smallest value in the image, within an optional mask.
auto dip::SampleStatistics(dip::Image const& in, dip::Image const& mask = {}) -> dip::StatisticsAccumulator
Computes the first four central moments of the pixel intensities, within an optional mask.
auto dip::Covariance(dip::Image const& in1, dip::Image const& in2, dip::Image const& mask = {}) -> dip::CovarianceAccumulator
Computes the covariance and correlation between the two images, within an optional mask.
auto dip::PearsonCorrelation(dip::Image const& in1, dip::Image const& in2, dip::Image const& mask = {}) -> dip::dfloat
Computes the Pearson correlation coefficient. See dip::Covariance.
auto dip::SpearmanRankCorrelation(dip::Image const& in1, dip::Image const& in2, dip::Image const& mask = {}) -> dip::dfloat
Computes the Spearman rank correlation coefficient.
auto dip::CenterOfMass(dip::Image const& in, dip::Image const& mask = {}) -> dip::FloatArray
Computes the center of mass (first order moments) of the image in, optionally using only those pixels selected by mask.
auto dip::Moments(dip::Image const& in, dip::Image const& mask = {}) -> dip::MomentAccumulator
Computes the first order moments and second order central moments of the image in, optionally using only those pixels selected by mask.

Function documentation

dip::uint dip::Count(dip::Image const& in, dip::Image const& mask = {})

Counts the number of non-zero pixels in a scalar image.

in must be scalar, but can have any data type. mask, if forged, must be of the same sizes as in, or be singleton expandable to that size, and must be binary.

dip::UnsignedArray dip::MaximumPixel(dip::Image const& in, dip::Image const& mask = {}, dip::String const& positionFlag = S::FIRST)

Returns the coordinates of the maximum pixel in the image.

The image must be scalar. If in is complex, the modulus of its values are used. If positionFlag is "first", the first maximum is found, in linear index order. If it is "last", the last one is found.

dip::UnsignedArray dip::MinimumPixel(dip::Image const& in, dip::Image const& mask = {}, dip::String const& positionFlag = S::FIRST)

Returns the coordinates of the minimum pixel in the image.

The image must be scalar. If in is complex, the modulus of its values are used. If positionFlag is "first", the first minimum is found, in linear index order. If it is "last", the last one is found.

void dip::CumulativeSum(dip::Image const& in, dip::Image const& mask, dip::Image& out, dip::BooleanArray const& process = {})

Calculates the cumulative sum of the pixel values over all those dimensions which are specified by process.

If process is an empty array, all dimensions are processed. The output is an image of the same size as the input. For tensor images, the output has the same tensor size and shape as the input.

If mask is forged, those pixels not selected by the mask are presumed to be 0.

dip::MinMaxAccumulator dip::MaximumAndMinimum(dip::Image const& in, dip::Image const& mask = {})

Finds the largest and smallest value in the image, within an optional mask.

If mask is not forged, all input pixels are considered. In case of a tensor image, returns the maximum and minimum sample values. In case of a complex samples, treats real and imaginary components as individual samples.

dip::StatisticsAccumulator dip::SampleStatistics(dip::Image const& in, dip::Image const& mask = {})

Computes the first four central moments of the pixel intensities, within an optional mask.

If mask is not forged, all input pixels are considered. In case of a tensor image, returns the statistics over all sample values. The image must be real-valued.

dip::CovarianceAccumulator dip::Covariance(dip::Image const& in1, dip::Image const& in2, dip::Image const& mask = {})

Computes the covariance and correlation between the two images, within an optional mask.

If mask is not forged, all input pixels are considered. In case of tensor images, returns the covariance over all sample values. The images must be real-valued and have the same number of tensor elements.

To compute the covariance or correlation between two channels in a multi-channel image (a tensor image):

Covariance( in[ 0 ], in[ 1 ], mask );

dip::dfloat dip::SpearmanRankCorrelation(dip::Image const& in1, dip::Image const& in2, dip::Image const& mask = {})

Computes the Spearman rank correlation coefficient.

If mask is not forged, all input pixels are considered. In case of tensor images, returns the Spearman rank correlation coefficient over all sample values. The images must be real-valued and have the same number of tensor elements.

To compute the Spearman rank correlation coefficient between two channels in a multi-channel image (a tensor image):

SpearmanRankCorrelation( in[ 0 ], in[ 1 ], mask );

dip::FloatArray dip::CenterOfMass(dip::Image const& in, dip::Image const& mask = {})

Computes the center of mass (first order moments) of the image in, optionally using only those pixels selected by mask.

If mask is not forged, all input pixels are considered. in must be scalar and real-valued.

dip::MomentAccumulator dip::Moments(dip::Image const& in, dip::Image const& mask = {})

Computes the first order moments and second order central moments of the image in, optionally using only those pixels selected by mask.

If mask is not forged, all input pixels are considered. in must be scalar and real-valued.