lesioncenters: Distinct Lesion Centers

In neuroconductor-devel/lesiontools: Statistical and Computational Tools for Brain Lesion Analysis

Description

This function finds the centers of distinct lesions based on a lesion probability map. The method is described in Dworkin et al., (2018).

Usage

lesioncenters(probmap, binmap, minCenterSize = 10, radius = 1,
  parallel = F, cores = 2)

Arguments

probmap	a 3D array or image of class nifti, containing the probability that each voxel is a lesion voxel
binmap	a 3D array or nifti mask in which voxels are classified as either lesion voxels or not lesion voxels. Note that mask should be in the same space as the probmap volume
minCenterSize	an integer value representing the minimum number of connected voxels that can be considered a lesion center
radius	an integer specifying radius of the neighborhood (in voxels) for which the hessian should be calculated.
parallel	is a logical value that indicates whether the user's computer is Linux or Unix (i.e. macOS), and should run the code in parallel
cores	if parallel = TRUE, cores is an integer value that indicates how many cores the function should be run on

Value

A list containing lesioncenters (a nifti file with labeled lesion centers) and lesioncount (an integer value representing the number of distinct lesions)

References

J.D. Dworkin, K.A. Linn, I. Oguz, G.M. Fleishman, R. Bakshi, G. Nair, P.A. Calabresi, R.G. Henry, J. Oh, N. Papinutto, D. Pelletier, W. Rooney, W. Stern, N.L. Sicotte, D.S. Reich, R.T. Shinohara. An automated statistical technique for counting distinct multiple sclerosis lesions. American Journal of Neuroradiology, 2018; 39, 626-633.

Examples

## Not run: 
library(neurobase)
lesion.probs <- readnii('path/to/probabilitymap')
centers <- lesioncenters(probmap = lesion.probs, binmap = lesion.probs>0.30,
                         parallel = TRUE, cores = 4) 
## End(Not run)

neuroconductor-devel/lesiontools documentation built on May 15, 2019, 3:16 p.m.