monai.transforms.intensity.array

A collection of “vanilla” transforms for intensity adjustment.

Classes

AdjustContrast(gamma[, invert_image, ...])	Changes image intensity with gamma transform. Each pixel/voxel intensity is updated as::.
ComputeHoVerMaps([dtype])	Compute horizontal and vertical maps from an instance mask It generates normalized horizontal and vertical distances to the center of mass of each region.
DetectEnvelope([axis, n])	Find the envelope of the input data along the requested axis using a Hilbert transform.
ForegroundMask([threshold, hsv_threshold, ...])	Creates a binary mask that defines the foreground based on thresholds in RGB or HSV color space.
GaussianSharpen([sigma1, sigma2, alpha, approx])	Sharpen images using the Gaussian Blur filter.
GaussianSmooth([sigma, approx])	Apply Gaussian smooth to the input data based on specified sigma parameter.
GibbsNoise([alpha])	The transform applies Gibbs noise to 2D/3D MRI images.
HistogramNormalize([num_bins, min, max, ...])	Apply the histogram normalization to input image.
IntensityRemap([kernel_size, slope])	Transform for intensity remapping of images.
KSpaceSpikeNoise(loc[, k_intensity])	Apply localized spikes in k-space at the given locations and intensities.
MaskIntensity([mask_data, select_fn])	Mask the intensity values of input image with the specified mask data.
MedianSmooth([radius])	Apply median filter to the input data based on specified radius parameter.
NormalizeIntensity([subtrahend, divisor, ...])	Normalize input based on the subtrahend and divisor: (img - subtrahend) / divisor.
RandAdjustContrast([prob, gamma, ...])	Randomly changes image intensity with gamma transform.
RandBiasField([degree, coeff_range, dtype, prob])	Random bias field augmentation for MR images.
RandCoarseDropout(holes, spatial_size[, ...])	Randomly coarse dropout regions in the image, then fill in the rectangular regions with specified value.
RandCoarseShuffle(holes, spatial_size[, ...])	Randomly select regions in the image, then shuffle the pixels within every region.
RandCoarseTransform(holes, spatial_size[, ...])	Randomly select coarse regions in the image, then execute transform operations for the regions.
RandGaussianNoise([prob, mean, std, dtype])	Add Gaussian noise to image.
RandGaussianSharpen([sigma1_x, sigma1_y, ...])	Sharpen images using the Gaussian Blur filter based on randomly selected sigma1, sigma2 and alpha.
RandGaussianSmooth([sigma_x, sigma_y, ...])	Apply Gaussian smooth to the input data based on randomly selected sigma parameters.
RandGibbsNoise([prob, alpha])	Naturalistic image augmentation via Gibbs artifacts.
RandHistogramShift([num_control_points, prob])	Apply random nonlinear transform to the image's intensity histogram.
RandIntensityRemap([prob, kernel_size, ...])	Transform for intensity remapping of images.
RandKSpaceSpikeNoise([prob, ...])	Naturalistic data augmentation via spike artifacts.
RandRicianNoise([prob, mean, std, ...])	Add Rician noise to image.
RandScaleIntensity(factors[, prob, ...])	Randomly scale the intensity of input image by v = v * (1 + factor) where the factor is randomly picked.
RandScaleIntensityFixedMean([prob, factors, ...])	Randomly scale the intensity of input image by v = v * (1 + factor) where the factor is randomly picked.
RandShiftIntensity(offsets[, safe, prob, ...])	Randomly shift intensity with randomly picked offset.
RandStdShiftIntensity(factors[, prob, ...])	Shift intensity for the image with a factor and the standard deviation of the image by: v = v + factor * std(v) where the factor is randomly picked.
SavitzkyGolaySmooth(window_length, order[, ...])	Smooth the input data along the given axis using a Savitzky-Golay filter.
ScaleIntensity([minv, maxv, factor, ...])	Scale the intensity of input image to the given value range (minv, maxv).
ScaleIntensityFixedMean([factor, ...])	Scale the intensity of input image by v = v * (1 + factor), then shift the output so that the output image has the same mean as the input.
ScaleIntensityRange(a_min, a_max[, b_min, ...])	Apply specific intensity scaling to the whole numpy array.
ScaleIntensityRangePercentiles(lower, upper, ...)	Apply range scaling to a numpy array based on the intensity distribution of the input.
ShiftIntensity(offset[, safe])	Shift intensity uniformly for the entire image with specified offset.
StdShiftIntensity(factor[, nonzero, ...])	Shift intensity for the image with a factor and the standard deviation of the image by: v = v + factor * std(v).
ThresholdIntensity(threshold[, above, cval])	Filter the intensity values of whole image to below threshold or above threshold.
UltrasoundConfidenceMapTransform([alpha, ...])	Compute confidence map from an ultrasound image.