mpicbg.imglib.algorithm.peak

Class GaussianPeakFitterND<T extends RealType<T>>

java.lang.Object

mpicbg.imglib.algorithm.peak.GaussianPeakFitterND<T>

Type Parameters:

T - the type of the given image, must extend RealType, for we operate on real values.

public class GaussianPeakFitterND<T extends RealType<T>>
extends Object

A simple class fit a gaussian to the peaks in a n-dim, 0-background image.

The gaussian can have a different sigma in any direction, but the ellipse main axes still need to be aligned with the image axes. Note that there is no constant term in this equation, thus the need for a 0-background image.

The fitting uses a plain Levenberg-Marquardt least-square curve fitting algorithm, with various small tweaks for robustness and speed, mainly a first step to derive a crude estimate, based on maximum-likelihood analytic formulae.

A fitter is instantiated for an image, but can be easily processed in parallel for multiple spots on this image.

Author:

Jean-Yves Tinevez (tinevez@pasteur.fr), 2011-2012

Constructor Summary

Constructors

Constructor and Description
GaussianPeakFitterND(Image<T> image) Instantiate a 2D gaussian peak fitter that will operate on the given image.

Method Summary

Modifier and Type	Method and Description
boolean	checkInput() Ensure the image is not null.
String	getErrorMessage()
double[]	process(Localizable point, double[] typical_sigma) Fit an elliptical gaussian to a peak in the image, using the formula:

Methods inherited from class java.lang.Object

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

Constructor Detail

GaussianPeakFitterND

public GaussianPeakFitterND(Image<T> image)

Instantiate a 2D gaussian peak fitter that will operate on the given image. It is important that the image has a 0 background for this class to operate properly.

Method Detail

checkInput

public boolean checkInput()

Ensure the image is not null.

process

public double[] process(Localizable point,
                        double[] typical_sigma)

Fit an elliptical gaussian to a peak in the image, using the formula:

        g(xᵢ) = A * exp ( - ∑ cᵢ × (xᵢ - x₀ᵢ)² )
 

First observation arrays are built by collecting pixel positions and intensities around the given peak location. These arrays are then used to guess a starting set of parameters, that is then fed to least-square optimization procedure, using the Levenberg-Marquardt curve fitter.

Calls to this function does not generate any class field, and can therefore by readily parallelized with multiple peaks on the same image.

Parameters:

point - the approximate coordinates of the peak

typical_sigma - the typical sigma of the peak (in pixel unit, array of one element per dimension), that will be used to derive the size of a block that will be inspected around the peak by the fit (the actual block size will of of size 2 * 2 * ceil(typical_sigma) + 1).

Returns:

a 2*ndims+1 elements double array containing fit estimates, in the following indices:

 0.                       A
 1 → ndims              x₀ᵢ
 ndims+1 → 2 × ndims    cᵢ = 1 / σᵢ² 

getErrorMessage

public String getErrorMessage()