trainableSegmentation.metrics

Class PixelError

java.lang.Object

trainableSegmentation.metrics.Metrics

trainableSegmentation.metrics.PixelError

public class PixelError
extends Metrics

This class implements the pixel error metric

Field Summary

Fields inherited from class trainableSegmentation.metrics.Metrics

originalLabels, proposedLabels, verbose

Constructor Summary

Constructors

Constructor and Description
PixelError(ij.ImagePlus originalLabels, ij.ImagePlus proposedLabels) Initialize pixel error metric

Method Summary

Modifier and Type	Method and Description
double	getMetricValue() Calculate the pixel error in 2D between some original labels and the corresponding proposed labels (without thresholding).
double	getMetricValue(double binaryThreshold) Calculate the pixel error in 2D between some original labels and the corresponding proposed labels.
Callable<Double>	getPixelErrorConcurrent(ij.process.ImageProcessor image1, ij.process.ImageProcessor image2) Get pixel error between two image in a concurrent way (to be submitted to an Executor Service).
Callable<Double>	getPixelErrorConcurrent(ij.process.ImageProcessor image1, ij.process.ImageProcessor image2, double binaryThreshold) Get pixel error between two image in a concurrent way (to be submitted to an Executor Service).
double	getPixelErrorMaximalFScore(double minThreshold, double maxThreshold, double stepThreshold) Get the best F-score of the pixel error over a set of thresholds
ClassificationStatistics	getPrecisionRecallStats(double binaryThreshold) Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
ArrayList<ClassificationStatistics>	getPrecisionRecallStats(double minThreshold, double maxThreshold, double stepThreshold) Calculate the precision-recall values based on pixel error between some 2D original labels and the corresponding proposed labels.
ClassificationStatistics	getPrecisionRecallStats(double binaryThreshold, ij.ImagePlus mask) Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
Callable<ClassificationStatistics>	getPrecisionRecallStatsConcurrent(ij.process.ImageProcessor image1, ij.process.ImageProcessor image2, double binaryThreshold) Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
Callable<ClassificationStatistics>	getPrecisionRecallStatsConcurrent(ij.process.ImageProcessor image1, ij.process.ImageProcessor image2, ij.process.ImageProcessor mask, double binaryThreshold) Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
ClassificationStatistics[]	getPrecisionRecallStatsPerSlice(double binaryThreshold) Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
static void	main(String[] args) Main method for calculate the pixel error metrics from the command line
ClassificationStatistics	precisionRecallStats(ij.process.ImageProcessor label, ij.process.ImageProcessor proposal, double binaryThreshold) Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels.
ClassificationStatistics	precisionRecallStats(ij.process.ImageProcessor label, ij.process.ImageProcessor proposal, ij.process.ImageProcessor mask, double binaryThreshold) Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels.

Methods inherited from class trainableSegmentation.metrics.Metrics

getMinimumMetricValue, setVerboseMode

Methods inherited from class java.lang.Object

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

Constructor Detail

PixelError

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

Initialize pixel error metric

Parameters:

originalLabels - original labels (single 2D image or stack)

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

Method Detail

getMetricValue

public double getMetricValue(double binaryThreshold)

Calculate the pixel error in 2D between some original labels and the corresponding proposed labels. Both image are binarized.

Specified by:

getMetricValue in class Metrics

Parameters:

binaryThreshold - threshold value to binarize proposal (larger than 0 and smaller than 1)

Returns:

pixel error

getPixelErrorConcurrent

public Callable<Double> getPixelErrorConcurrent(ij.process.ImageProcessor image1,
                                                ij.process.ImageProcessor image2,
                                                double binaryThreshold)

Get pixel error between two image in a concurrent way (to be submitted to an Executor Service). Both images are binarized.

Parameters:

image1 - first image

image2 - second image

binaryThreshold - threshold to apply to both images

Returns:

pixel error

getMetricValue

public double getMetricValue()

Calculate the pixel error in 2D between some original labels and the corresponding proposed labels (without thresholding).

Returns:

pixel error

getPixelErrorConcurrent

public Callable<Double> getPixelErrorConcurrent(ij.process.ImageProcessor image1,
                                                ij.process.ImageProcessor image2)

Get pixel error between two image in a concurrent way (to be submitted to an Executor Service).

Parameters:

image1 - first image

image2 - second image

Returns:

pixel error

getPrecisionRecallStats

public ArrayList<ClassificationStatistics> getPrecisionRecallStats(double minThreshold,
                                                                   double maxThreshold,
                                                                   double stepThreshold)

Calculate the precision-recall values based on pixel error between some 2D original labels and the corresponding proposed labels.

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:

pixel error value and derived statistics for each threshold

getPrecisionRecallStats

public ClassificationStatistics getPrecisionRecallStats(double binaryThreshold)

Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels. Both images are binarized.

Parameters:

binaryThreshold - threshold value to binarize proposal (larger than 0 and smaller than 1)

Returns:

pixel error value and derived statistics

getPrecisionRecallStatsPerSlice

public ClassificationStatistics[] getPrecisionRecallStatsPerSlice(double binaryThreshold)

Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels. Both images are binarized and the results are presented per slice.

Parameters:

binaryThreshold - threshold value to binarize proposal (larger than 0 and smaller than 1)

Returns:

pixel error value and derived statistics per input slice

getPrecisionRecallStats

public ClassificationStatistics getPrecisionRecallStats(double binaryThreshold,
                                                        ij.ImagePlus mask)

Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels. Both images are binarized.

Parameters:

binaryThreshold - threshold value to binarize proposal (larger than 0 and smaller than 1)

mask - mask image

Returns:

pixel error value and derived statistics

getPrecisionRecallStatsConcurrent

public Callable<ClassificationStatistics> getPrecisionRecallStatsConcurrent(ij.process.ImageProcessor image1,
                                                                            ij.process.ImageProcessor image2,
                                                                            double binaryThreshold)

Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service). Both images are binarized.

Parameters:

image1 - first image

image2 - second image

binaryThreshold - threshold to apply to both images

Returns:

pixel error value and derived statistics

getPrecisionRecallStatsConcurrent

public Callable<ClassificationStatistics> getPrecisionRecallStatsConcurrent(ij.process.ImageProcessor image1,
                                                                            ij.process.ImageProcessor image2,
                                                                            ij.process.ImageProcessor mask,
                                                                            double binaryThreshold)

Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service). Both images are binarized.

Parameters:

image1 - first image

image2 - second image

mask - mask image

binaryThreshold - threshold to apply to both images

Returns:

pixel error value and derived statistics

precisionRecallStats

public ClassificationStatistics precisionRecallStats(ij.process.ImageProcessor label,
                                                     ij.process.ImageProcessor proposal,
                                                     double binaryThreshold)

Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels. Both image are binarized.

Parameters:

label - 2D image with the original labels

proposal - 2D image with the proposed labels

binaryThreshold - threshold value to binarize the input images

Returns:

rand index value and derived statistics

precisionRecallStats

public ClassificationStatistics precisionRecallStats(ij.process.ImageProcessor label,
                                                     ij.process.ImageProcessor proposal,
                                                     ij.process.ImageProcessor mask,
                                                     double binaryThreshold)

Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels. Both image are binarized.

Parameters:

label - 2D image with the original labels

proposal - 2D image with the proposed labels

mask - 2D image representing the binary mask

binaryThreshold - threshold value to binarize the input images

Returns:

classification statistics

getPixelErrorMaximalFScore

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

Get the best F-score of the pixel error 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:

maximal F-score of the pixel error

main

public static void main(String[] args)

Main method for calculate the pixel error metrics from the command line

Parameters:

args - arguments to decide the action