partition

partition,LogicalNeuroVol,integer-method

partition,LogicalNeuroVol,numeric-method

partition,DenseNeuroVol,numeric-method

This function partitions an image into a set of disjoint clusters using k-means clustering.

A collection of data structures and methods for handling volumetric
brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient
representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array
implementations, memory-mapped file access for large datasets, and
spatial transformation capabilities. Implements methods for image resampling,
spatial filtering, region of interest analysis, and connected component labeling. General introduction
to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis",
<ISBN:9781108795760>).

Bradley Buchsbaum

neuroim2

Data Structures for Brain Imaging Data

partition function

<dl><dt>x</dt>
<dd>the image to partition, represented as a 3D array.</dd>
<dt>k</dt>
<dd>the number of clusters to form.</dd>
<dt>...</dt>
<dd>additional arguments passed to the kmeans function.</dd></dl>

Arguments

Partition an image into a set of disjoint clusters — partition

<dl>

<dt>x</dt>
<dd>the image to partition, represented as a 3D array.</dd>


<dt>k</dt>
<dd>the number of clusters to form.</dd>


<dt>...</dt>
<dd>additional arguments passed to the kmeans function.</dd>

</dl>

partition: Partition an image into a set of disjoint clusters

Description

Usage

Value

Arguments

See Also

Examples