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::.

ClipIntensityPercentiles(lower, upper[, ...])

Apply clip based on the intensity distribution of input image.

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.