filters

Collection of functions for altering images based on structural properties

This module contains a variety of functions for altering images based on the structural characteristics, such as pore sizes. A definition of a filter is a function that returns an image the same shape as the original image, but with altered values.

filters.apply_chords(im[, spacing, axis, ...])	Adds chords to the void space in the specified direction.
filters.apply_chords_3D(im[, spacing, ...])	Adds chords to the void space in all three principle directions.
filters.apply_padded(im, pad_width, func[, ...])	Applies padding to an image before sending to func, then extracts the result corresponding to the original image shape.
filters.chunked_func(func[, overlap, divs, ...])	Performs the specfied operation "chunk-wise" in parallel using dask.
filters.distance_transform_lin(im[, axis, mode])	Replaces each void voxel with the linear distance to the nearest solid voxel along the specified axis.
filters.fftmorphology(im, strel[, mode])	Perform morphological operations on binary images using fft approach for improved performance
filters.fill_blind_pores(im[, conn, surface])	Fills all blind pores that are isolated from the main void space.
filters.find_disconnected_voxels(im[, conn, ...])	Identifies all voxels that are not connected to the edge of the image.
filters.find_dt_artifacts(dt)	Label points in a distance transform that are closer to image boundary than solid
filters.find_peaks(dt[, r_max, strel, ...])	Finds local maxima in the distance transform
filters.find_trapped_regions(im, seq, outlets)	Find the trapped regions given an invasion sequence map and specified outlets
filters.flood(im, labels[, mode])	Floods/fills each region in an image with a single value based on the specific values in that region.
filters.flood_func(im, func[, labels])	Flood each isolated region in an image with a constant value calculated by the given function.
filters.hold_peaks(im[, axis, ascending])	Replaces each voxel with the highest value along the given axis.
filters.imagej	imagej
filters.local_thickness(im[, sizes, mode, divs])	For each voxel, this function calculates the radius of the largest sphere that both engulfs the voxel and fits entirely within the foreground.
filters.nl_means_layered(im[, cores, axis, ...])	Apply the non-local means filter to each 2D layer of a stack in parallel.
filters.nphase_border(im[, include_diagonals])	Identifies the voxels in regions that border N other regions.
filters.pc_to_satn(pc, im[, mode])	Converts an image of capillary entry pressures to saturation values
filters.porosimetry(im[, sizes, inlets, ...])	Performs a porosimetry simulution on an image.
filters.prune_branches(skel[, ...])	Remove all dangling ends or tails of a skeleton
filters.reduce_peaks(peaks)	Any peaks that are broad or elongated are replaced with a single voxel that is located at the center of mass of the original voxels.
filters.region_size(im[, strel])	Replace each voxel with the size of the region to which it belongs
filters.satn_to_seq(satn[, im, mode])	Converts an image of nonwetting phase saturations to invasion sequence values
filters.seq_to_satn(seq[, im, mode])	Converts an image of invasion sequence values to invading phase saturation values.
filters.size_to_satn(size[, im, bins, mode])	Converts an image of invasion size values into non-wetting phase saturations.
filters.size_to_seq(size[, im, bins, mode])	Converts an image of invasion size values into invasion sequence values
filters.snow_partitioning(im[, dt, r_max, ...])	Partition the void space into pore regions using a marker-based watershed algorithm
filters.snow_partitioning_n(im[, r_max, ...])	This function partitions an imaging oontain an arbitrary number of phases into regions using a marker-based watershed segmentation.
filters.snow_partitioning_parallel(im[, ...])	Performs SNOW algorithm in parallel (or serial) to reduce time (or memory usage) by geomertirc domain decomposition of large images.
filters.trim_disconnected_blobs(im, inlets)	Removes foreground voxels not connected to specified inlets.
filters.trim_extrema(im, h[, mode])	Trims local extrema in greyscale values by a specified amount.
filters.trim_floating_solid(im[, conn, surface])	Removes all solid that that is not attached to main solid structure.
filters.trim_nearby_peaks(peaks, dt[, f])	Removes peaks that are nearer to another peak than to solid
filters.trim_nonpercolating_paths(im, ...[, ...])	Remove all nonpercolating paths between specified locations
filters.trim_saddle_points(peaks, dt[, maxiter])	Removes peaks that were mistakenly identified because they lied on a saddle or ridge in the distance transform that was not actually a true local peak.
filters.trim_small_clusters(im[, size])	Remove isolated voxels or clusters of a given size or smaller

Modules

imagej