Image filter and manipulation (medpy.filter)

This package contains various image filters and image manipulation functions.

Smoothing medpy.filter.smoothing

Image smoothing / noise reduction in grayscale images.

anisotropic_diffusion(img[, niter, kappa, …]) Edge-preserving, XD Anisotropic diffusion.
gauss_xminus1d(img, sigma[, dim]) Applies a X-1D gauss to a copy of a XD image, slicing it along dim.

Binary medpy.filter.binary

Binary image manipulation.

size_threshold(img, thr[, comp, structure]) Removes binary objects from an image identified by a size threshold.
largest_connected_component(img[, structure]) Select the largest connected binary component in an image.
bounding_box(img) Return the bounding box incorporating all non-zero values in the image.

Image medpy.filter.image

Grayscale image manipulation.

sls(minuend, subtrahend[, metric, noise, …]) Computes the signed local similarity between two images.
ssd(minuend, subtrahend[, normalized, …]) Computes the sum of squared difference (SSD) between patches of minuend and subtrahend.
average_filter(input[, size, footprint, …]) Calculates a multi-dimensional average filter.
sum_filter(input[, size, footprint, output, …]) Calculates a multi-dimensional sum filter.
local_minima(img[, min_distance]) Returns all local minima from an image.
otsu(img[, bins]) Otsu’s method to find the optimal threshold separating an image into fore- and background.
resample(img, hdr, target_spacing[, …]) Re-sample an image to a new voxel-spacing.

Label medpy.filter.label

Label map manipulation.

relabel_map(label_image, mapping[, key]) Relabel an image using the supplied mapping.
relabel(label_image[, start]) Relabel the regions of a label image.
relabel_non_zero(label_image[, start]) Relabel the regions of a label image.
fit_labels_to_mask(label_image, mask) Reduces a label images by overlaying it with a binary mask and assign the labels either to the mask or to the background.

Noise medpy.filter.noise

Global and local noise estimation in grayscale images.

immerkaer(input[, mode, cval]) Estimate the global noise.
immerkaer_local(input, size[, output, mode, …]) Estimate the local noise.
separable_convolution(input, weights[, …]) Calculate a n-dimensional convolution of a separable kernel to a n-dimensional input.

Utilities medpy.filter.utilities

Utilities to apply filters selectively and create your own ones.

xminus1d(img, fun, dim, *args, **kwargs) Applies the function fun along all X-1D dimensional volumes of the images img dimension dim.
intersection(i1, h1, i2, h2) Returns the intersecting parts of two images in real world coordinates.
pad(input[, size, footprint, output, mode, cval]) Returns a copy of the input, padded by the supplied structuring element.

Hough transform medpy.filter.houghtransform

The hough transform shape detection algorithm.

ght(img, template) Implementation of the general hough transform for all dimensions.
ght_alternative(img, template, indices) Alternative implementation of the general hough transform, which uses iteration over indices rather than broadcasting rules like ght.
template_ellipsoid(shape) Returns an ellipsoid binary structure of a of the supplied radius that can be used as template input to the generalized hough transform.
template_sphere(radius, dimensions) Returns a spherical binary structure of a of the supplied radius that can be used as template input to the generalized hough transform.

Intensity range standardization medpy.filter.IntensityRangeStandardization

A learning method to align the intensity ranges of images.

IntensityRangeStandardization([cutoffp, …]) Class to standardize intensity ranges between a number of images.