find_initial_points: Find Initial Cluster Centers for Supervoxel Algorithm

In bbuchsbaum/neurocluster: Spatially contrained clustering for neuroimaging data

Find Initial Cluster Centers for Supervoxel Algorithm

Description

This function finds the initial cluster centers for a supervoxel algorithm. Supervoxels are used to partition 3D image data into volumetric regions, grouping similar voxels together. The initial cluster centers are crucial for the performance and quality of the final supervoxels.

Usage

find_initial_points(cds, grad, K = 100)

Arguments

cds	A matrix or data frame representing the spatial coordinates of the voxels.
grad	A vector representing the gradient values of the voxels.
K	The desired number of supervoxels (clusters) in the output (default: 100).

Value

A list containing two elements: selected - a vector of the selected indices corresponding to the initial cluster centers, coords - a matrix or data frame with the spatial coordinates of the initial cluster centers.

bbuchsbaum/neurocluster documentation built on April 1, 2024, 8:43 p.m.