fuzzySpatialCMeansSegmentation

Fuzzy spatial c-means for image segmentation.

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 progress.

Value

list containing segmentation and probability images

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.

Author

NJ Tustison

Examples

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