Closed ntustison closed 3 years ago
Hey @stnava,
In working on this histograms paper, I implemented a version of the fuzzy spatial c-means since I couldn't find it anywhere. It's pretty quick
fuzzySpatialCMeansSegmentation <- function( image, mask, numberOfClusters = 4, m = 2, p = 1, q = 1, radius = 2, maxNumberOfIterations = 20, convergenceThreshold = 0.2, verbose = FALSE )
seg <- fuzzySpatialCMeansSegmentation( image, mask, numberOfClusters = 3, convergenceThreshold = 0.01, verbose = TRUE ) Cluster centers: 63.5 127 190.5 Iteration 1 (out of 20) --- Dice overlap = 0 Iteration 2 (out of 20) --- Dice overlap = 0.8941908 Iteration 3 (out of 20) --- Dice overlap = 0.9218776 Iteration 4 (out of 20) --- Dice overlap = 0.9493425 Iteration 5 (out of 20) --- Dice overlap = 0.9635466 Iteration 6 (out of 20) --- Dice overlap = 0.9742551 Iteration 7 (out of 20) --- Dice overlap = 0.9824668 Iteration 8 (out of 20) --- Dice overlap = 0.9885702 Iteration 9 (out of 20) --- Dice overlap = 0.9902902
Was wondering if it'd be worth having in ANTsR?
yes - I have a few implementations of this lying around as well but never added it ... probably worth having due to speed.
Okay, will put that in.
https://github.com/ANTsX/ANTsR/pull/341
Hey @stnava,
In working on this histograms paper, I implemented a version of the fuzzy spatial c-means since I couldn't find it anywhere. It's pretty quick
Was wondering if it'd be worth having in ANTsR?