fuzzySpatialCMeansSegmentation: fuzzySpatialCMeansSegmentation

In neuroconductor-devel/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Description

Fuzzy spatial c-means for image segmentation.

Usage

fuzzySpatialCMeansSegmentation(
  image,
  mask = NULL,
  numberOfClusters = 4,
  m = 2,
  p = 1,
  q = 1,
  radius = 2,
  maxNumberOfIterations = 20,
  convergenceThreshold = 0.02,
  verbose = FALSE
)

Arguments

image	image to be segmented.
mask	optional mask image. Otherwise, the entire image is used.
numberOfClusters	number of segmentation clusters
m	fuzziness parameter (default = 2).
p	membership importance parameter (default = 1).
q	spatial constraint importance parameter (default = 1). q = 0 is equivalent to conventional fuzzy c-means.
radius	neighborhood radius (scalar or array) for spatial constraint.
maxNumberOfIterations	iteration limit (default = 20).
convergenceThreshold	Convergence between iterations is measured using the Dice coefficient (default = 0.02).
verbose	print diagnostics to the screen.

Details

Image segmentation using fuzzy spatial c-means as described in

Chuang et al., Fuzzy c-means clustering with spatial information for image segmentation. CMIG: 30:9-15, 2006.

Value

list containing segmentation and probability images

Author(s)

NJ Tustison

Examples

image <- antsImageRead( getANTsRData( "r16" ) )
mask <- getMask( image )
fuzzy <- fuzzySpatialCMeansSegmentation( image, mask )

neuroconductor-devel/ANTsR documentation built on April 1, 2021, 1:02 p.m.