net.sf.ij_plugins.clustering

Class KMeans2D

java.lang.Object

net.sf.ij_plugins.clustering.KMeans2D

public final class KMeans2D
extends java.lang.Object

2D version of the k-means algorithm. Works with multi-band images.

Author:

Jarek Sacha

Constructor Summary

Constructors

Constructor and Description
KMeans2D()
KMeans2D(KMeansConfig config)

Method Summary

Methods

Modifier and Type	Method and Description
int	closestCluster(float[] x) Find index of the cluster closest to the sample x.
protected void	clusterAnimationAddCurrent(java.lang.String title)
protected void	clusterAnimationInitialize()
protected ij.ImageStack	encodeCentroidValueImage()
ij.ImageStack	getCentroidValueImage() Returns stack where discovered clusters can be represented by replacing pixel values in a cluster by the value of the centroid of that cluster.
ij.ImageStack	getClusterAnimation() Return stack representing clustering optimization.
float[][]	getClusterCenters() Return location of cluster centers.
long	getNumberOfStepsToConvergence()
protected float[][]	initializeClusterCenters() Initialize clusters using k-means++ approach, see http://en.wikipedia.org/wiki/K-means++
protected java.util.Iterator<float[]>	newPixelIterator()
protected int	numberOfValues() Number of values in a pixel
ij.process.ByteProcessor	run(ij.ImageStack stack) Perform k-means clustering of the input stack.
protected boolean	supportsClusterAnimation()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

KMeans2D

public KMeans2D()

KMeans2D

public KMeans2D(KMeansConfig config)

Method Detail

run

public ij.process.ByteProcessor run(ij.ImageStack stack)

Perform k-means clustering of the input stack. Elements of the stack must be of type FloatProcessor.

Parameters:

stack - stack representing a multi-band image.

Returns:

segmented image.

getClusterAnimation

public ij.ImageStack getClusterAnimation()

Return stack representing clustering optimization. This will return not null value only when configuration parameters clusterAnimationEnabled is set to true.

Returns:

stack representing cluster optimization, can return null.

numberOfValues

protected int numberOfValues()

Number of values in a pixel

encodeCentroidValueImage

protected ij.ImageStack encodeCentroidValueImage()

newPixelIterator

protected java.util.Iterator<float[]> newPixelIterator()

initializeClusterCenters

protected float[][] initializeClusterCenters()

Initialize clusters using k-means++ approach, see http://en.wikipedia.org/wiki/K-means++

Returns:

initial cluster centers.

supportsClusterAnimation

protected boolean supportsClusterAnimation()

clusterAnimationInitialize

protected void clusterAnimationInitialize()

clusterAnimationAddCurrent

protected void clusterAnimationAddCurrent(java.lang.String title)

getCentroidValueImage

public final ij.ImageStack getCentroidValueImage()

Returns stack where discovered clusters can be represented by replacing pixel values in a cluster by the value of the centroid of that cluster.

Returns:

centroid value image

getNumberOfStepsToConvergence

public final long getNumberOfStepsToConvergence()

getClusterCenters

public final float[][] getClusterCenters()

Return location of cluster centers.

Returns:

array of cluster centers. First index refers to cluster number.

closestCluster

public final int closestCluster(float[] x)

Find index of the cluster closest to the sample x. run(ij.ImageStack) must be run before calling this method.

Parameters:

x - test point, number of values must be the same is input stack size.

Returns:

index of the closest cluster.

See Also:

run(ij.ImageStack)