summitWatershed {PathwaySpace} | R Documentation |
Variation of the watershed algorithm for summit detection.
Description
The summitWatershed
function implements a segmentation
strategy to identify summits within a landscape image generated by the
PathwaySpace package. This function is entirely coded in R, which helps
alleviating users from the task of loading an excessive number of
dependencies. Nonetheless, while this novel implementation prevents
the burden a 'dependency heaviness', it still requires optimization
as it currently exhibits slower performance compared to well-established
implementations such as the watershed function from the EBImage package.
The summitWatershed
maintain a certain level of compatibility
with the EBImage's watershed function, and both can be used in the
PathwaySpace package.
Usage
summitWatershed(x, tolerance = 0.1, ext = 1)
Arguments
x |
A 2D-numeric array in which each point represents the coordinates of a signal in a landscape image. |
tolerance |
The minimum signal intensity of a summit (in [0,1]), representing a fraction of the maximum signal intensity. |
ext |
Radius (in pixels) for detecting neighboring objects. |
Value
A matrix with labeled summits.
Author(s)
Vinicius Chagas, Victor Apolonio, and Mauro Castro (mauro.castro@ufpr.br)
See Also
Examples
# Load a demo landscape image
data('gimage', package = 'PathwaySpace')
# Scale down the image for a quicker demonstration
gimage <- gimage[200:300, 200:300]
# Check signal range
range(gimage, na.rm = TRUE)
# [1] 0 1
# Check image
image(gimage)
# Threshold the signal intensity, for example:
gimage[gimage < 0.5] <- 0
# Run summit segmentation
gmask <- summitWatershed(x = gimage)
# Check resulting image mask
image(gimage)