trainableSegmentation.metrics

Class Metrics

java.lang.Object

trainableSegmentation.metrics.Metrics

Direct Known Subclasses:

AdjustedRandError, PixelError, RandError, VariationOfInformation, WarpingError

public abstract class Metrics
extends Object

This is the mother class for 2D segmentation metrics

Field Summary

Fields

Modifier and Type	Field and Description
protected ij.ImagePlus	originalLabels original labels (single 2D image or stack)
protected ij.ImagePlus	proposedLabels proposed new labels (single 2D image or stack of the same as as the original labels)
protected boolean	verbose boolean flag to set the level of detail on the standard output messages

Constructor Summary

Constructors

Constructor and Description
Metrics(ij.ImagePlus originalLabels, ij.ImagePlus proposedLabels)

Method Summary

Modifier and Type	Method and Description
abstract double	getMetricValue(double binaryThreshold)
double	getMinimumMetricValue(double minThreshold, double maxThreshold, double stepThreshold) Get the minimum metric value over a set of thresholds
void	setVerboseMode(boolean verbose) Set verbose mode

Methods inherited from class java.lang.Object

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

Field Detail

originalLabels

protected ij.ImagePlus originalLabels

original labels (single 2D image or stack)

proposedLabels

protected ij.ImagePlus proposedLabels

proposed new labels (single 2D image or stack of the same as as the original labels)

verbose

protected boolean verbose

boolean flag to set the level of detail on the standard output messages

Constructor Detail

Metrics

public Metrics(ij.ImagePlus originalLabels,
               ij.ImagePlus proposedLabels)

Method Detail

getMetricValue

public abstract double getMetricValue(double binaryThreshold)

setVerboseMode

public void setVerboseMode(boolean verbose)

Set verbose mode

Parameters:

verbose - true to display more information in the standard output

getMinimumMetricValue

public double getMinimumMetricValue(double minThreshold,
                                    double maxThreshold,
                                    double stepThreshold)

Get the minimum metric value over a set of thresholds

Parameters:

minThreshold - minimum threshold value to binarize the input images

maxThreshold - maximum threshold value to binarize the input images

stepThreshold - threshold step value to use during binarization

Returns:

minimum value of the metric