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Connected components

Source: R/all_generic.R, R/neurovol.R
conn_comp-methods.Rd

Find connected components in an image. This function identifies and labels spatially connected regions in neuroimaging data, supporting both binary masks and thresholded volumes.

Usage

conn_comp(x, ...)

# S4 method for class 'NeuroVol'
conn_comp(
  x,
  threshold = 0,
  cluster_table = TRUE,
  local_maxima = TRUE,
  local_maxima_dist = 15,
  ...
)

Arguments

x

the image object

...

additional arguments including:

  • threshold - numeric value defining lower intensity bound for image mask

  • cluster_table - logical indicating whether to return cluster statistics

  • local_maxima - logical indicating whether to compute local maxima

  • local_maxima_dist - minimum distance between local maxima

  • connect - connectivity pattern ("26-connect", "18-connect", or "6-connect")

threshold

threshold defining lower intensity bound for image mask

cluster_table

return cluster_table

local_maxima

return table of local maxima

local_maxima_dist

the distance used to define minum distance between local maxima

Value

A list containing:

  • index - A ClusteredNeuroVol object with cluster labels

  • size - A NeuroVol object with cluster sizes

  • voxels - A list of cluster voxel coordinates

  • cluster_table - (optional) Data frame with cluster statistics

  • local_maxima - (optional) Matrix of local maxima coordinates

An object representing the connected components of x.

Examples

# Create a simple 3D volume with two distinct regions
space <- NeuroSpace(c(10,10,10), c(1,1,1))
vol_data <- array(0, c(10,10,10))

# Create first cluster in corner (2x2x2)
vol_data[1:2, 1:2, 1:2] <- 1

# Create second cluster in opposite corner (2x2x2)
vol_data[8:9, 8:9, 8:9] <- 1

# Create NeuroVol object
vol <- NeuroVol(vol_data, space)

# Find connected components with default 26-connectivity
# Returns components above threshold 0
comps <- conn_comp(vol, threshold=0)

# Access results
max(comps$index) == 2  # Should have 2 clusters
#> [1] TRUE
all(comps$size >= 0)    # All clusters should have >= 0
#> [1] TRUE

# Get cluster statistics
comps <- conn_comp(vol, threshold=0, cluster_table=TRUE)
# cluster_table contains: index, x, y, z, N (size), Area, value

# Find local maxima within clusters
comps <- conn_comp(vol, threshold=0, local_maxima=TRUE,
                  local_maxima_dist=2)
# local_maxima contains: index, x, y, z, value