| Package | Description |
|---|---|
| trainableSegmentation.metrics | |
| trainableSegmentation.utils |
| Modifier and Type | Method and Description |
|---|---|
static ClassificationStatistics |
RandError.adaptedRandIndexStats3D(ij.ImagePlus originalLabels,
ij.ImagePlus proposedLabels)
Calculate the Rand index between to 3D clusters, as described by
William M.
|
ClassificationStatistics |
RandError.getForegroundRestrictedRandAfterThinningStats(double binaryThreshold,
boolean perSliceAverage)
Calculate the foreground-restricted Rand score (N^2 normalization) and
its derived statistics in 2D between some original labels and the
corresponding proposed labels after border thinning.
|
ClassificationStatistics |
RandError.getForegroundRestrictedRandAfterThinningStatsN2(ij.process.ImageProcessor label,
ij.process.ImageProcessor proposal,
double binaryThreshold)
Calculate the foreground-restricted Rand precision and recall between
some 2D original labels and the corresponding proposed labels with N^2
normalization.
|
ClassificationStatistics |
RandError.getForegroundRestrictedRandIndexStats(double binaryThreshold)
Calculate the foreground-restricted Rand error (N^2 normalization) and
its derived statistics in 2D between some original labels and the
corresponding proposed labels.
|
ClassificationStatistics |
RandError.getForegroundRestrictedRandIndexStatsN2(ij.process.ImageProcessor label,
ij.process.ImageProcessor proposal,
double binaryThreshold)
Calculate the foreground-restricted Rand precision and recall between
some 2D original labels and the corresponding proposed labels with N^2
normalization.
|
ClassificationStatistics |
RandError.getForegroundRestrictedRandIndexStatsN2(ij.process.ShortProcessor cluster1,
ij.process.ShortProcessor cluster2)
Get foreground-restricted Rand statistics (precision and recall) with
N^2 normalization.
|
ClassificationStatistics[] |
RandError.getForegroundRestrictedRandIndexStatsPerSlice(double binaryThreshold)
Calculate the foreground-restricted Rand index (N^2 normalization) and
its derived statistics in 2D between some original labels and the
corresponding proposed labels.
|
ClassificationStatistics |
PixelError.getPrecisionRecallStats(double binaryThreshold)
Calculate the pixel error and its derived statistics in 2D between
some original labels and the corresponding proposed labels.
|
ClassificationStatistics |
PixelError.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.
|
ClassificationStatistics[] |
PixelError.getPrecisionRecallStatsPerSlice(double binaryThreshold)
Calculate the pixel error and its derived statistics in 2D between
some original labels and the corresponding proposed labels.
|
ClassificationStatistics[] |
WarpingError.getPrecisionRecallStatsSplitsAndMergersPerSlice(double th,
int radius,
boolean bordersArePositive)
Calculate the precision-recall values based on pixel error between
some warped 2D original labels and the corresponding proposed labels
(taking into account only the split and merger pixels).
|
ClassificationStatistics |
RandError.getRandIndexStats(double binaryThreshold)
Calculate the standard Rand index and its derived statistics in 2D between
some original labels and the corresponding proposed labels.
|
ClassificationStatistics |
RandError.getRandIndexStats(ij.process.ShortProcessor cluster1,
ij.process.ShortProcessor cluster2)
Calculate the standard Rand index between to clusters, as described by
William M.
|
ClassificationStatistics |
PixelError.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 |
PixelError.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.
|
ClassificationStatistics |
RandError.randIndexStats(ij.process.ImageProcessor label,
ij.process.ImageProcessor proposal,
double binaryThreshold)
Calculate the standard Rand index between some 2D original labels
and the corresponding proposed labels.
|
| Modifier and Type | Method and Description |
|---|---|
ArrayList<ClassificationStatistics> |
WarpingError.getDualPrecisionRecallStats(double minThreshold,
double maxThreshold,
double stepThreshold)
Calculate the precision-recall values based on pixel error between
some warped 2D original labels and the corresponding proposed labels
in both directions (from original labels to proposal and reversely).
|
ArrayList<ClassificationStatistics> |
RandError.getForegroundRestrictedRandAfterThinningStats(double minThreshold,
double maxThreshold,
double stepThreshold,
boolean perSliceAverage)
Calculate the precision-recall values based on the foreground-restricted
Rand score between some 2D original labels and the corresponding
proposed labels (after border thinning).
|
Callable<ClassificationStatistics> |
RandError.getForegroundRestrictedRandAfterThinningStatsConcurrent(ij.process.ImageProcessor image1,
ij.process.ImageProcessor image2,
double binaryThreshold)
Get foreground-restricted Rand score value and derived statistics
between two images in a concurrent way (to be submitted to an Executor
Service).
|
ArrayList<ClassificationStatistics> |
RandError.getForegroundRestrictedRandIndexStats(double minThreshold,
double maxThreshold,
double stepThreshold)
Calculate the precision-recall values based on the foreground-restricted
Rand index between some 2D original labels and the corresponding
proposed labels.
|
Callable<ClassificationStatistics> |
RandError.getForegroundRestrictedRandIndexStatsConcurrent(ij.process.ImageProcessor image1,
ij.process.ImageProcessor image2,
double binaryThreshold)
Get foreground-restricted Rand index value and derived statistics
between two images in a concurrent way (to be submitted to an Executor
Service).
|
ArrayList<ClassificationStatistics> |
WarpingError.getPrecisionRecallStats(double minThreshold,
double maxThreshold,
double stepThreshold)
Calculate the precision-recall values based on pixel error between
some warped 2D original labels and the corresponding proposed labels.
|
ArrayList<ClassificationStatistics> |
PixelError.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.
|
Callable<ClassificationStatistics> |
PixelError.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> |
PixelError.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).
|
ArrayList<ClassificationStatistics> |
WarpingError.getPrecisionRecallStatsSplitsAndMergers(double minThreshold,
double maxThreshold,
double stepThreshold,
int radius,
boolean bordersArePositive,
boolean visualize)
Calculate the precision-recall values based on pixel error between
some warped 2D original labels and the corresponding proposed labels
(taking into account only the split and merger pixels).
|
ArrayList<ClassificationStatistics> |
RandError.getRandIndexStats(double minThreshold,
double maxThreshold,
double stepThreshold)
Calculate the precision-recall values based on standard Rand index
between some 2D original labels and the corresponding proposed labels.
|
ArrayList<ClassificationStatistics> |
WarpingError.getRandIndexStats(double minThreshold,
double maxThreshold,
double stepThreshold)
Calculate the precision-recall values based on Rand index between
some warped 2D original labels and the corresponding proposed labels.
|
Callable<ClassificationStatistics> |
RandError.getRandIndexStatsConcurrent(ij.process.ImageProcessor image1,
ij.process.ImageProcessor image2,
double binaryThreshold)
Get standard Rand index value and derived statistics between two images
in a concurrent way (to be submitted to an Executor Service).
|
| Modifier and Type | Method and Description |
|---|---|
static double |
Utils.getKappa(ClassificationStatistics stats)
Get Kappa statistic
|
| Modifier and Type | Method and Description |
|---|---|
static Plot |
Utils.createPrecisionRecallPlot(ArrayList<ClassificationStatistics> stats)
Create plot with the precision-recall curve
|
static double |
Utils.getPrecRecArea(ArrayList<ClassificationStatistics> stats)
Get area under the Precision/Recall curve
|
static double |
Utils.getROCArea(ArrayList<ClassificationStatistics> stats)
Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
|
static void |
Utils.plotPrecisionRecall(ArrayList<ClassificationStatistics> stats)
Plot the precision-recall curve
|
static void |
Utils.plotROC(ArrayList<ClassificationStatistics> stats)
Plot the Receiver operating characteristic curve
|
Copyright © 2010–2024 Fiji. All rights reserved.