haussdorff_distance(xs, ys, aggregate=maximum)

Returns, for two segmented masks, the Haussdorff distance (per object).

Agg=maximum is the default, returns the maximum symmetric Euclidean distance of X to Y

aszero(xs)

zeros(eltype(xs), sizes(xs))

colocalize(xs, ys; metric="pearson", windowsize=3)

Compute a colocalization metric `metric` on a window size of `windowsize^2`.

Set `windowsize` to >= 3, odd. If set to -1, use the entire image, output will be single scalar.

See `metrics` for metrics to use.

colocalize_all(xs, ys; windowsize=3)

Apply all coloc metrics. Returns a dictionary, so results are stored in results[metric].
Runs multithreaded.
```julia
xs = rand(10, 10)
ys = rand(10, 10)
res =colocalize_all(xs, ys)
correlation_values = res["pearson"]
μ, md, σ, min, max, q1, q3, iqr, q95, q99, nans = describe_map(correlation_values)
```

compute_distances_cc_to_mask(from_cc, to_mask)

Compute distances, overlap, and a distance map from a component map to a mask.
The distance map has a token 0.1 if the distance = 0 to prevent occlusion with background.

describe_array(xs)

Returns μ, md, σ, min, max, q1, q3, iqr, q95, q99, nans for `xs`.

filter_projection(image, w=1, z=0)

Compute a 2-stage filtered mask based on image. w is the window size, z is the number of standard deviations to use as a threshold.
This removes isolated localizations of low intensity.

haussdorff_distance(xs, ys, aggregate=maximum)

Returns, for two segmented masks, the Haussdorff distance (per object).

Agg=maximum is the default, returns the maximum symmetric Euclidean distance of X to Y

haussdorff_max(xs, ys)

The reference implementation for the Haussdorff distance, returns the maximum symmetric Euclidean distance of X to Y
See https://en.wikipedia.org/wiki/Hausdorff_distance

haussdorff_mean(xs, ys)

A variant of the Haussdorff distance, returns the mean symmetric Euclidean distance of X to Y
See https://en.wikipedia.org/wiki/Hausdorff_distance

intersection_mask(xs, ys)

mask where x .> 0 and y .> 0

list_metrics()

Returns a list of the supported metrics

metrics_iterator()

Returns an iterator over the supported metrics, in String / Function pairs.

Metric functions expect equal shaped array, and return a scalar value

segment(image, scale=1.0)

Perform basic otsu thresholding. Scale argument allows you to include more (>1) background or less (<1)

spear(left, right)
Return the spearman correlation between arguments, and the significance (z-test, t-test)

Reuse with permission from https://github.com/bencardoen/SubPrecisionContactDetection.jl

tomask(img)

Utility function to binarize argument

union_distance_mask(xs, ys, distance)

Finds the connected components in X that are <= distance to any component in Y and vice versa

Returns mask_x, mask_y

union_mask(xs, ys)

mask where any component that has a non-zero intersection is set to 1.