neuroSCC (version 1.0.0)

neuroContour: Obtain and save neuroimaging contours from a NIFTI file

Description

This function extracts contours from a neuroimaging NIFTI file where values change according to specified levels. It processes the NIFTI file with neuroCleaner to extract structured neuroimaging data, then extracts contours using contoureR::getContourLines. These contours serve as input for Triangulation::TriMesh, which is used in Simultaneous Confidence Corridors (SCCs) calculations.

While not mandatory, it is highly recommended that the input NIFTI file be pre-processed such that zero values represent the background and non-zero values represent regions of interest. The function's default behavior extracts contours at level 0, which is ideal for well-masked data.

Usage

neuroContour(niftiFile, paramZ = 35, levels = c(0), plotResult = FALSE)

Value

A list of data frames, where each data frame contains the x and y coordinates of a contour. The first element typically represents the external boundary, while subsequent elements (if present) represent internal contours or holes. Each data frame has two columns:

• x – x-coordinates of the contour points.

• y – y-coordinates of the contour points.

Arguments

niftiFile

character, the path to the NIFTI file containing neuroimaging data. Ideally, the file should be masked so that zero values represent the background.

paramZ

integer, the specific z-slice to extract contours from. Default is 35.

levels

numeric, a vector of levels at which to draw the contours. Default is c(0).

plotResult

logical, if TRUE, plots the extracted contours. Default is FALSE.

Details

This function extracts contours from a NIFTI file, typically a masked image where background values are set to zero, and regions of interest contain non-zero values. While users can specify a different boundary level, the recommended approach is to use levels = 0 for masked data.

The extracted contours are typically used as input to Triangulation::TriMesh to create a triangular mesh of the region, which is then used for Simultaneous Confidence Corridors calculations.

See Also

getContourLines for the underlying contour extraction.
Triangulation::TriMesh for the next step in the SCC calculation process.

Examples

# Get the file path for a sample NIfTI file
niftiFile <- system.file("extdata", "syntheticControl1.nii.gz", package = "neuroSCC")

# Extract contours at level 0
contours <- neuroContour(niftiFile, paramZ = 35, levels = 0, plotResult = TRUE)

# Display the first few points of the main contour
head(contours[[1]])