java.lang.Object

inra.ijpb.label.LabelImages

Direct Known Subclasses:: LabelImages

public class LabelImages
extends java.lang.Object

Utility methods for label images (stored as 8-, 16- or 32-bits). Most methods within this class apply an operator on the input label map(s). Some of them may return a newly created label map. The LabelUtils class provides additional utility methods, more dedicated to plugin creators.

Author:: David Legland
See Also:: LabelUtils

Method Summary

Modifier and Type	Method	Description
`static ij.ImagePlus`	`applyLut(ij.ImagePlus labelImage, double[] values)`	Applies the given Look-up table to the input label image.
`static ij.ImageStack`	`applyLut(ij.ImageStack labelImage, double[] values)`	Applies the given Look-up table to the input 3D label image.
`static ij.process.FloatProcessor`	`applyLut(ij.process.ImageProcessor labelImage, double[] values)`	Applies the given Look-up table to the input 2D label image.
`static ij.process.ImageProcessor`	`areaOpening(ij.process.ImageProcessor labelImage, int nPixelMin)`	Applies area opening on a label image: creates a new label image that contains only particles with at least the specified number of pixels.
`static ij.process.ImageProcessor`	`binarize(ij.process.ImageProcessor image, int label)`	Returns the binary image with value equals to `true` only when the corresponding value in the input image equals `label`.
`static ij.process.ImageProcessor`	`createLabelImage(int width, int height, int nLabels)`	Creates a label image with the appropriate class to store the required number of labels.
`static ij.process.ByteProcessor`	`createLabelImage(ij.process.ImageProcessor... images)`	Creates a new label image from a set of binary images.
`static ij.ImageStack`	`createLabelStack(int width, int height, int depth, int nLabels)`	Creates a new image stack with the appropriate class to store the required number of labels.
`static ij.ImagePlus`	`cropLabel(ij.ImagePlus imagePlus, int label, int border)`	Returns a binary image that contains only the selected particle or region, by automatically cropping the image and eventually adding some borders.
`static ij.ImageStack`	`cropLabel(ij.ImageStack image, int label, int border)`	Returns a binary image that contains only the selected particle or region, by automatically cropping the image and eventually adding some borders.
`static ij.process.ImageProcessor`	`cropLabel(ij.process.ImageProcessor image, int label, int border)`	Returns a binary image that contains only the selected particle or region, by automatically cropping the image and eventually adding some borders.
`static ij.ImagePlus`	`dilateLabels(ij.ImagePlus imagePlus, double distMax)`	Applies a constrained dilation to each region in the 3D label map: The dilation of each region is constrained by the other regions; The dilation extent is limited by the specified distance (in voxel unit)
`static ij.ImageStack`	`dilateLabels(ij.ImageStack labelMap, double radius)`	Applies a constrained dilation to each region in the 3D label map: The dilation of each region is constrained by the other regions; The dilation extent is limited by the the radius value (in voxel unit).
`static ij.process.ImageProcessor`	`dilateLabels(ij.process.ImageProcessor labelMap, double radius)`	Applies a constrained dilation to each region in the label map: The dilation of each region is constrained by the other regions; The dilation extent is given by a radius value (in pixel unit).
`static ij.ImageStack`	`distanceMap(ij.ImageStack image)`	Computes the 3D distance map from an image of labels.
`static ij.ImageStack`	`distanceMap(ij.ImageStack image, float[] weights, boolean normalize)`	Computes the distance map from a 3D image of labels Distance is computed for each label voxel, as the chamfer distance to the nearest voxel with a different value.
`static ij.ImageStack`	`distanceMap(ij.ImageStack image, short[] weights, boolean normalize)`	Computes the distance map from a 3D image of labels Distance is computed for each label voxel, as the chamfer distance to the nearest voxel with a different value.
`static ij.ImageStack`	`distanceMap(ij.ImageStack image, ChamferMask3D mask, boolean floatingPoint, boolean normalize)`	Computes the 3D distance map from an image of labels, by specifying the chamfer mask and the normalization.
`static ij.process.ImageProcessor`	`distanceMap(ij.process.ImageProcessor image)`	Computes the distance map for each region within a label map.
`static ij.process.ImageProcessor`	`distanceMap(ij.process.ImageProcessor image, float[] weights, boolean normalize)`	Computes the distance map from a 3D image of labels Distance is computed for each label voxel, as the chamfer distance to the nearest voxel with a different value.
`static ij.process.ImageProcessor`	`distanceMap(ij.process.ImageProcessor image, short[] weights, boolean normalize)`	Computes the distance map for each region within a label map.
`static ij.process.ImageProcessor`	`distanceMap(ij.process.ImageProcessor image, ChamferMask2D mask, boolean floatingPoint, boolean normalize)`	Computes the distance map for each region within a label map, by specifying the chamfer mask and the normalization.
`static int[]`	`findAllLabels(ij.ImagePlus image)`	Returns the set of unique labels existing in the given image, excluding the value zero (used for background).
`static int[]`	`findAllLabels(ij.ImageStack image)`	Returns the set of unique labels existing in the given stack, excluding the value zero (used for background).
`static int[]`	`findAllLabels(ij.process.ImageProcessor image)`	Returns the set of unique labels existing in the given image, excluding the value zero (used for background).
`static int`	`findLargestLabel(ij.ImagePlus imagePlus)`	Find largest label (by number of pixels/voxels) in input image
`static java.awt.Point[]`	`findPositionOfMaxValues(ij.process.ImageProcessor valueImage, ij.process.ImageProcessor labelImage, int[] labels)`	Deprecated. use LavelValues.findPositionOfMaxValues instead
`static java.awt.Point[]`	`findPositionOfMinValues(ij.process.ImageProcessor valueImage, ij.process.ImageProcessor labelImage, int[] labels)`	Deprecated. use LavelValues.findPositionOfMinValues instead
`static double`	`getDiceCoefficient(ij.ImagePlus labelImage1, ij.ImagePlus labelImage2)`	Get the Dice coefficient between two label images.
`static double`	`getDiceCoefficient(ij.ImageStack labelImage1, ij.ImageStack labelImage2)`	Get the Dice coefficient between two label images.
`static double`	`getDiceCoefficient(ij.process.ImageProcessor labelImage1, ij.process.ImageProcessor labelImage2)`	Get the Dice coefficient between two label images.
`static ij.measure.ResultsTable`	`getDiceCoefficientPerLabel(ij.ImagePlus labelImage1, ij.ImagePlus labelImage2)`	Get the Dice coefficient per label (intersection over union overlap) between two label images.
`static ij.measure.ResultsTable`	`getDiceCoefficientPerLabel(ij.ImageStack labelImage1, ij.ImageStack labelImage2)`	Get the Dice coefficient per label (intersection over union overlap) between two label images.
`static ij.measure.ResultsTable`	`getDiceCoefficientPerLabel(ij.process.ImageProcessor labelImage1, ij.process.ImageProcessor labelImage2)`	Get the Dice coefficient per label (intersection over union overlap) between two label images.
`static double`	`getFalseNegativeError(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)`	Get the total false negative error between two label images (source and target).
`static double`	`getFalseNegativeError(ij.ImageStack sourceImage, ij.ImageStack targetImage)`	Get the total false negative error between two label images (source and target).
`static double`	`getFalseNegativeError(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)`	Get the total false negative error between two label images (source and target).
`static ij.measure.ResultsTable`	`getFalseNegativeErrorPerLabel(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)`	Get the false negative error between two label images (source and target) per each individual labeled region.
`static ij.measure.ResultsTable`	`getFalseNegativeErrorPerLabel(ij.ImageStack sourceImage, ij.ImageStack targetImage)`	Get the false negative error between two label images (source and target) per each individual labeled region.
`static ij.measure.ResultsTable`	`getFalseNegativeErrorPerLabel(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)`	Get the false negative error between two label images (source and target) per each individual labeled region.
`static double`	`getFalsePositiveError(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)`	Get the total false positive error between two label images (source and target).
`static double`	`getFalsePositiveError(ij.ImageStack sourceImage, ij.ImageStack targetImage)`	Get the total false positive error between two label images (source and target).
`static double`	`getFalsePositiveError(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)`	Get the total false positive error between two label images (source and target).
`static ij.measure.ResultsTable`	`getFalsePositiveErrorPerLabel(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)`	Get the false positive error between two label images (source and target) per each individual labeled region.
`static ij.measure.ResultsTable`	`getFalsePositiveErrorPerLabel(ij.ImageStack sourceImage, ij.ImageStack targetImage)`	Get the false positive error between two label images (source and target) per each individual labeled region.
`static ij.measure.ResultsTable`	`getFalsePositiveErrorPerLabel(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)`	Get the false positive error between two label images (source and target) per each individual labeled region.
`static double`	`getJaccardIndex(ij.ImagePlus labelImage1, ij.ImagePlus labelImage2)`	Get the Jaccard index (intersection over union overlap) between two label images.
`static double`	`getJaccardIndex(ij.ImageStack labelImage1, ij.ImageStack labelImage2)`	Get the Jaccard index (intersection over union overlap) between two label images.
`static double`	`getJaccardIndex(ij.process.ImageProcessor labelImage1, ij.process.ImageProcessor labelImage2)`	Get the Jaccard index (intersection over union overlap) between two label images.
`static ij.measure.ResultsTable`	`getJaccardIndexPerLabel(ij.ImagePlus labelImage1, ij.ImagePlus labelImage2)`	Get the Jaccard index per label (intersection over union overlap) between two label images.
`static ij.measure.ResultsTable`	`getJaccardIndexPerLabel(ij.ImageStack labelImage1, ij.ImageStack labelImage2)`	Get the Jaccard index per label (intersection over union overlap) between two label images.
`static ij.measure.ResultsTable`	`getJaccardIndexPerLabel(ij.process.ImageProcessor labelImage1, ij.process.ImageProcessor labelImage2)`	Get the Jaccard index per label (intersection over union overlap) between two label images.
`static java.util.ArrayList<java.lang.Float>`	`getSelectedLabels(ij.ImagePlus labelImage, ij.gui.Roi roi)`	Get list of selected labels in label image.
`static ij.measure.ResultsTable`	`getTargetOverlapPerLabel(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)`	Get the target overlap between two label images (source and target) per each individual labeled region.
`static ij.measure.ResultsTable`	`getTargetOverlapPerLabel(ij.ImageStack sourceImage, ij.ImageStack targetImage)`	Get the target overlap between two label images (source and target) per each individual labeled region.
`static ij.measure.ResultsTable`	`getTargetOverlapPerLabel(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)`	Get the target overlap between two label images (source and target) per each individual labeled region.
`static double`	`getTotalOverlap(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)`	Get the total overlap between two label images (source and target).
`static double`	`getTotalOverlap(ij.ImageStack sourceImage, ij.ImageStack targetImage)`	Get the total overlap between two label images (source and target).
`static double`	`getTotalOverlap(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)`	Get the total overlap between two label images (source and target).
`static double`	`getVolumeSimilarity(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)`	Get the total volume similarity between two label images (source and target).
`static double`	`getVolumeSimilarity(ij.ImageStack sourceImage, ij.ImageStack targetImage)`	Get the total volume similarity between two label images (source and target).
`static double`	`getVolumeSimilarity(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)`	Get the total volume similarity between two label images (source and target).
`static ij.measure.ResultsTable`	`getVolumeSimilarityPerLabel(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)`	Get the volume similarity between two label images (source and target) per each individual labeled region.
`static ij.measure.ResultsTable`	`getVolumeSimilarityPerLabel(ij.ImageStack sourceImage, ij.ImageStack targetImage)`	Get the volume similarity between two label images (source and target) per each individual labeled region.
`static ij.measure.ResultsTable`	`getVolumeSimilarityPerLabel(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)`	Get the volume similarity between two label images (source and target) per each individual labeled region.
`static boolean`	`isLabelImageType(ij.ImagePlus imagePlus)`	Checks if the input image may be a label image, and returns false if the type is not valid for label images.
`static ij.ImagePlus`	`keepLabels(ij.ImagePlus imagePlus, int[] labels)`	Creates a new image containing only the specified labels.
`static ij.ImageStack`	`keepLabels(ij.ImageStack image, int[] labels)`	Creates a new image containing only the specified labels.
`static ij.process.ImageProcessor`	`keepLabels(ij.process.ImageProcessor image, int[] labels)`	Creates a new image containing only the specified labels.
`static ij.ImagePlus`	`keepLargestLabel(ij.ImagePlus imagePlus)`	Returns a binary image that contains only the largest label.
`static ij.ImageStack`	`keepLargestLabel(ij.ImageStack image)`	Returns a binary image that contains only the largest label.
`static ij.process.ImageProcessor`	`keepLargestLabel(ij.process.ImageProcessor image)`	Returns a binary image that contains only the largest label.
`static ij.ImageStack`	`labelBoundaries(ij.ImageStack image)`	Creates a binary 3D image that contains 255 for voxels that are boundaries between two labels.
`static ij.process.ImageProcessor`	`labelBoundaries(ij.process.ImageProcessor image)`	Creates a binary 2D image that contains 255 for pixels that are boundaries between two labels.
`static ij.ImagePlus`	`labelToRgb(ij.ImagePlus imagePlus, byte[][] lut, java.awt.Color bgColor)`	Creates a new Color image from a label image, a LUT, and a color for background.
`static ij.ImageStack`	`labelToRgb(ij.ImageStack image, byte[][] lut, java.awt.Color bgColor)`	Creates a new Color image stack from a label image stack, a LUT, and a color for background.
`static ij.process.ColorProcessor`	`labelToRgb(ij.process.ImageProcessor image, byte[][] lut, java.awt.Color bgColor)`	Creates a new Color image from a label planar image, a LUT, and a color for background.
`static java.util.HashMap<java.lang.Integer,java.lang.Integer>`	`mapLabelIndices(int[] labels)`	Creates an associative array to retrieve the index corresponding to each label.
`static void`	`mergeLabels(ij.ImagePlus labelImage, ij.gui.Roi roi, boolean verbose)`	Merge labels selected by freehand or point tool.
`static void`	`mergeLabelsWithGap(ij.ImagePlus imagePlus, float[] labels, float newLabel, int conn)`	Merge several regions identified by their label, filling the gap between former regions.
`static void`	`mergeLabelsWithGap(ij.ImagePlus labelImage, ij.gui.Roi roi, int conn, boolean verbose)`	Merge labels selected by freehand or point tool.
`static void`	`mergeLabelsWithGap(ij.ImageStack image, float[] labels, float newLabel, int conn)`	Merge several regions identified by their label, filling the gap between former regions.
`static void`	`mergeLabelsWithGap(ij.process.ImageProcessor image, float[] labels, float newLabel, int conn)`	Merge several regions identified by their label, filling the gap between former regions.
`static int[]`	`pixelCount(ij.ImagePlus image, int[] labels)`	Computes the number of pixels (or voxels) composing each regions in the 2D or 3D label image.
`static int[]`	`pixelCount(ij.process.ImageProcessor image, int[] labels)`	Computes the number of pixels composing each particle in the label image.
`static ij.ImagePlus`	`regionComponentsLabeling(ij.ImagePlus imagePlus, int regionLabel, int conn, int bitDepth)`	Computes the labels in the binary 2D or 3D image contained in the given ImagePlus, and computes the maximum label to set up the display range of the resulting ImagePlus.
`static ij.ImageStack`	`regionComponentsLabeling(ij.ImageStack image, int regionLabel, int conn, int bitDepth)`	Computes the labels of the connected components in the given 3D binary image.
`static ij.process.ImageProcessor`	`regionComponentsLabeling(ij.process.ImageProcessor image, int regionLabel, int conn, int bitDepth)`	Computes the labels of the connected components in the given planar binary image.
`static void`	`remapLabels(ij.ImagePlus imagePlus)`	Ensures that the labels in the given label image range from 1 to Lmax.
`static void`	`remapLabels(ij.ImageStack image)`	Ensures that the labels in the given label image range from 1 to Lmax.
`static void`	`remapLabels(ij.process.ImageProcessor image)`	Ensures that the labels in the given label image range from 1 to Lmax.
`static void`	`removeBorderLabels(ij.ImagePlus imagePlus)`	Removes all regions that touch the borders of the image.
`static void`	`removeBorderLabels(ij.ImageStack image)`	Removes all regions that touch the borders of the image.
`static void`	`removeBorderLabels(ij.process.ImageProcessor image)`	Removes all regions that touch the borders of the image.
`static void`	`removeLabels(ij.ImagePlus labelImage, ij.gui.Roi roi, boolean verbose)`	Remove labels selected by freehand or point ROIs (in place).
`static void`	`removeLargestLabel(ij.ImagePlus imagePlus)`	Removes the regions corresponding to the largest label from a label image.
`static void`	`removeLargestLabel(ij.ImageStack image)`	Removes the regions corresponding to the largest label from a label image.
`static void`	`removeLargestLabel(ij.process.ImageProcessor image)`	Removes the regions corresponding to the largest label from a label image.
`static void`	`replaceLabels(ij.ImagePlus imagePlus, float[] labels, float newLabel)`	Replace all values specified in label array by the value 0.
`static void`	`replaceLabels(ij.ImagePlus imagePlus, int[] labels, int newLabel)`	Replace all values specified in label array by a new value.
`static void`	`replaceLabels(ij.ImageStack image, float[] labels, float newLabel)`	Replace all values specified in label array by the specified value.
`static void`	`replaceLabels(ij.ImageStack image, int[] labels, int newLabel)`	Replace all values specified in label array by a new value.
`static void`	`replaceLabels(ij.process.ImageProcessor image, float[] labels, float newLabel)`	Replace all values specified in label array by the value 0.
`static void`	`replaceLabels(ij.process.ImageProcessor image, int[] labels, int newLabel)`	Replace all values specified in label array by a new value.
`static ij.ImagePlus`	`sizeOpening(ij.ImagePlus labelImage, int minElementCount)`	Applies size opening on a label image: creates a new label image that contains only particles with at least the specified number of pixels or voxels.
`static ij.ImageStack`	`volumeOpening(ij.ImageStack labelImage, int nVoxelMin)`	Applies area opening on a 3D label image: creates a new label image that contains only particle with at least the specified number of voxels.
`static int[]`	`voxelCount(ij.ImageStack image, int[] labels)`	Counts the number of voxels that compose each labeled particle in 3D image.

Methods inherited from class java.lang.Object

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

Method Details
- isLabelImageType
  
  public static final boolean isLabelImageType(ij.ImagePlus imagePlus)
  
  Checks if the input image may be a label image, and returns false if the type is not valid for label images.
  
  Parameters:
  
  imagePlus - the input image to check
  
  Returns:
  
  true if the type of the input image is valid for label images
- createLabelImage
  
  public static final ij.process.ImageProcessor createLabelImage(int width, int height, int nLabels)
  
  Creates a label image with the appropriate class to store the required number of labels.
  
  Parameters:
  
  width - the width of the new label image
  
  height - the height of the new label image
  
  nLabels - expected number of labels in new image
  
  Returns:
  
  a new ImageProcessor with type adapted to store the expected number of labels
- createLabelStack
  
  public static final ij.ImageStack createLabelStack(int width, int height, int depth, int nLabels)
  
  Creates a new image stack with the appropriate class to store the required number of labels.
  
  Parameters:
  
  width - the width of the new label image
  
  height - the height of the new label image
  
  depth - the depth (number of slices) of the new label image
  
  nLabels - expected number of labels in new image
  
  Returns:
  
  a new ImageStack with type adapted to store the expected number of labels
- createLabelImage
  
  public static final ij.process.ByteProcessor createLabelImage(ij.process.ImageProcessor... images)
  Creates a new label image from a set of binary images. The label values range between 1 and the number of images.
  Example:
  ImageProcessor binary1 = new ByteProcessor(10, 10); binary1.set(2, 2, 255); binary1.set(3, 5, 255); ImageProcessor binary2 = new ByteProcessor(10, 10); binary2.set(4, 4, 255); binary2.set(3, 5, 255); // overlap of binary images ImageProcessor binary3 = new ByteProcessor(10, 10); binary3.set(6, 6, 255); ImageProcessor labels = LabelImages.createLabelImage(binary1, binary2, binary3); int background = labels.get(1, 1); // returns 0 int label1 = labels.get(2, 2); // returns 1 int label2 = labels.get(4, 4); // returns 2 int label3 = labels.get(6, 6); // returns 3 int label1overlap2 = labels.get(3, 5); // returns 2
  Parameters:
  
  images - a collection of binary images (0: background, >0 pixel belongs to current label)
  
  Returns:
  
  a new image with label values
- labelBoundaries
  
  public static final ij.ImageStack labelBoundaries(ij.ImageStack image)
  
  Creates a binary 3D image that contains 255 for voxels that are boundaries between two labels.
  
  Parameters:
  
  image - a 3D image containing label regions
  
  Returns:
  
  a new 3D binary image with white voxels at label boundaries
- labelBoundaries
  
  public static final ij.process.ImageProcessor labelBoundaries(ij.process.ImageProcessor image)
  
  Creates a binary 2D image that contains 255 for pixels that are boundaries between two labels.
  
  Parameters:
  
  image - a 2D image containing label regions
  
  Returns:
  
  a new 2D binary image with white pixels at label boundaries
- binarize
  
  public static final ij.process.ImageProcessor binarize(ij.process.ImageProcessor image, int label)
  
  Returns the binary image with value equals to true only when the corresponding value in the input image equals label.
  
  Parameters:
  
  image - the input label map
  
  label - the label of the region to binarize. Using a value equal to zero binarizes the background.
  
  Returns:
  
  a binary image of the selected label.
- cropLabel
  
  public static final ij.ImagePlus cropLabel(ij.ImagePlus imagePlus, int label, int border)
  
  Returns a binary image that contains only the selected particle or region, by automatically cropping the image and eventually adding some borders.
  
  Parameters:
  
  imagePlus - an image containing label of particles
  
  label - the label of the particle to select
  
  border - the number of pixels to add to each side of the particle
  
  Returns:
  
  a smaller binary image containing only the selected particle
- cropLabel
  
  public static final ij.process.ImageProcessor cropLabel(ij.process.ImageProcessor image, int label, int border)
  
  Returns a binary image that contains only the selected particle or region, by automatically cropping the image and eventually adding some borders.
  
  Parameters:
  
  image - a, image containing label of particles
  
  label - the label of the particle to select
  
  border - the number of pixels to add to each side of the particle
  
  Returns:
  
  a smaller binary image containing only the selected particle
- cropLabel
  
  public static final ij.ImageStack cropLabel(ij.ImageStack image, int label, int border)
  
  Returns a binary image that contains only the selected particle or region, by automatically cropping the image and eventually adding some borders.
  
  Parameters:
  
  image - a 3D image containing label of particles
  
  label - the label of the particle to select
  
  border - the number of voxels to add to each side of the particle
  
  Returns:
  
  a smaller binary image containing only the selected particle
- sizeOpening
  
  public static final ij.ImagePlus sizeOpening(ij.ImagePlus labelImage, int minElementCount)
  
  Applies size opening on a label image: creates a new label image that contains only particles with at least the specified number of pixels or voxels.
  
  Parameters:
  
  labelImage - an image of label regions
  
  minElementCount - the minimal number of pixels or voxels of regions
  
  Returns:
  
  a new image containing only regions with enough elements
  
  See Also:
  
  areaOpening(ImageProcessor, int), volumeOpening(ImageStack, int)
- areaOpening
  
  public static final ij.process.ImageProcessor areaOpening(ij.process.ImageProcessor labelImage, int nPixelMin)
  
  Applies area opening on a label image: creates a new label image that contains only particles with at least the specified number of pixels.
  
  Parameters:
  
  labelImage - an image of label regions
  
  nPixelMin - the minimal number of pixels of regions
  
  Returns:
  
  a new image containing only regions with enough pixels
- volumeOpening
  
  public static final ij.ImageStack volumeOpening(ij.ImageStack labelImage, int nVoxelMin)
  
  Applies area opening on a 3D label image: creates a new label image that contains only particle with at least the specified number of voxels. Keep original labels unchanged.
  
  Parameters:
  
  labelImage - an image of label regions
  
  nVoxelMin - the minimal number of voxels of regions
  
  Returns:
  
  a new image containing only regions with enough voxels
- labelToRgb
  
  public static final ij.ImagePlus labelToRgb(ij.ImagePlus imagePlus, byte[][] lut, java.awt.Color bgColor)
  
  Creates a new Color image from a label image, a LUT, and a color for background.
  
  Parameters:
  
  imagePlus - a 2D or 3D image containing labels and 0 for background
  
  lut - the array of color components for each label
  
  bgColor - the background color
  
  Returns:
  
  a new Color image
- labelToRgb
  
  public static final ij.process.ColorProcessor labelToRgb(ij.process.ImageProcessor image, byte[][] lut, java.awt.Color bgColor)
  
  Creates a new Color image from a label planar image, a LUT, and a color for background.
  
  Parameters:
  
  image - an ImageProcessor with label values and 0 for background
  
  lut - the array of color components for each label
  
  bgColor - the background color
  
  Returns:
  
  a new instance of ColorProcessor
- labelToRgb
  
  public static final ij.ImageStack labelToRgb(ij.ImageStack image, byte[][] lut, java.awt.Color bgColor)
  
  Creates a new Color image stack from a label image stack, a LUT, and a color for background.
  
  Parameters:
  
  image - an ImageStack with label values and 0 for background
  
  lut - the array of color components for each label
  
  bgColor - the background color
  
  Returns:
  
  a new instance of ImageStack containing color processors
- removeBorderLabels
  
  public static final void removeBorderLabels(ij.ImagePlus imagePlus)
  
  Removes all regions that touch the borders of the image.
  
  Parameters:
  
  imagePlus - a label image
- removeBorderLabels
  
  public static final void removeBorderLabels(ij.process.ImageProcessor image)
  
  Removes all regions that touch the borders of the image.
  
  Parameters:
  
  image - a label image
- removeBorderLabels
  
  public static final void removeBorderLabels(ij.ImageStack image)
  
  Removes all regions that touch the borders of the image.
  
  Parameters:
  
  image - a label image
- keepLargestLabel
  
  public static final ij.ImagePlus keepLargestLabel(ij.ImagePlus imagePlus)
  
  Returns a binary image that contains only the largest label.
  
  Parameters:
  
  imagePlus - an instance of ImagePlus containing a label image
  
  Returns:
  
  a new ImagePlus containing only the largest label
- keepLargestLabel
  
  public static final ij.process.ImageProcessor keepLargestLabel(ij.process.ImageProcessor image)
  
  Returns a binary image that contains only the largest label.
  
  Parameters:
  
  image - a label image
  
  Returns:
  
  a new image containing only the largest label
  
  Throws:
  
  java.lang.RuntimeException - if the image is empty
- keepLargestLabel
  
  public static final ij.ImageStack keepLargestLabel(ij.ImageStack image)
  
  Returns a binary image that contains only the largest label.
  
  Parameters:
  
  image - a label image
  
  Returns:
  
  a new image containing only the largest label
  
  Throws:
  
  java.lang.RuntimeException - if the image is empty
- removeLargestLabel
  
  public static final void removeLargestLabel(ij.ImagePlus imagePlus)
  
  Removes the regions corresponding to the largest label from a label image.
  
  Parameters:
  
  imagePlus - a label image
- removeLargestLabel
  
  public static final void removeLargestLabel(ij.process.ImageProcessor image)
  
  Removes the regions corresponding to the largest label from a label image.
  
  Parameters:
  
  image - a label image
- removeLargestLabel
  
  public static final void removeLargestLabel(ij.ImageStack image)
  
  Removes the regions corresponding to the largest label from a label image.
  
  Parameters:
  
  image - a 3D label image
- pixelCount
  
  public static final int[] pixelCount(ij.ImagePlus image, int[] labels)
  
  Computes the number of pixels (or voxels) composing each regions in the 2D or 3D label image.
  
  Parameters:
  
  image - a label image (2D or 3D)
  
  labels - the array of label indices to process
  
  Returns:
  
  an array the same size as labels, containing the number of pixels / voxels within each region
- pixelCount
  
  public static final int[] pixelCount(ij.process.ImageProcessor image, int[] labels)
  
  Computes the number of pixels composing each particle in the label image.
  
  Parameters:
  
  image - a label image
  
  labels - the array of label indices to process
  
  Returns:
  
  an array the same size as labels, containing the number of pixels of each region
  
  See Also:
  
  GeometricMeasures2D.area(ij.process.ImageProcessor, int[], double[])
- voxelCount
  
  public static final int[] voxelCount(ij.ImageStack image, int[] labels)
  
  Counts the number of voxels that compose each labeled particle in 3D image.
  
  Parameters:
  
  image - a label image
  
  labels - the array of label indices to process
  
  Returns:
  
  an array the same size as labels, containing the number of voxels of each region
  
  See Also:
  
  GeometricMeasures3D.volume(ij.ImageStack, int[], double[])
- findLargestLabel
  
  public static final int findLargestLabel(ij.ImagePlus imagePlus)
  
  Find largest label (by number of pixels/voxels) in input image
  
  Parameters:
  
  imagePlus - input image
  
  Returns:
  
  value of the largest label in the input label image
- findAllLabels
  
  public static final int[] findAllLabels(ij.ImagePlus image)
  
  Returns the set of unique labels existing in the given image, excluding the value zero (used for background). #see inra.ijpb.label.edit.FindAllLabels
  
  Parameters:
  
  image - an instance of ImagePlus containing a label image
  
  Returns:
  
  the list of unique labels present in image (without background)
- findAllLabels
  
  public static final int[] findAllLabels(ij.ImageStack image)
  
  Returns the set of unique labels existing in the given stack, excluding the value zero (used for background). #see inra.ijpb.label.edit.FindAllLabels
  
  Parameters:
  
  image - a 3D label image
  
  Returns:
  
  the list of unique labels present in image (without background)
- findAllLabels
  
  public static final int[] findAllLabels(ij.process.ImageProcessor image)
  
  Returns the set of unique labels existing in the given image, excluding the value zero (used for background). #see inra.ijpb.label.edit.FindAllLabels
  
  Parameters:
  
  image - a label image
  
  Returns:
  
  the list of unique labels present in image (without background)
- replaceLabels
  
  public static final void replaceLabels(ij.ImagePlus imagePlus, int[] labels, int newLabel)
  
  Replace all values specified in label array by a new value. This method changes directly the values within the image.
  
  Parameters:
  
  imagePlus - an ImagePlus containing a 3D label image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
- replaceLabels
  
  public static final void replaceLabels(ij.ImagePlus imagePlus, float[] labels, float newLabel)
  
  Replace all values specified in label array by the value 0. This method changes directly the values within the image.
  
  Parameters:
  
  imagePlus - an ImagePlus containing a 3D label image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
- replaceLabels
  
  public static final void replaceLabels(ij.process.ImageProcessor image, int[] labels, int newLabel)
  
  Replace all values specified in label array by a new value.
  
  Parameters:
  
  image - a label planar image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
- replaceLabels
  
  public static final void replaceLabels(ij.process.ImageProcessor image, float[] labels, float newLabel)
  
  Replace all values specified in label array by the value 0.
  
  Parameters:
  
  image - a label planar image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
- replaceLabels
  
  public static final void replaceLabels(ij.ImageStack image, int[] labels, int newLabel)
  
  Replace all values specified in label array by a new value.
  
  Parameters:
  
  image - a label 3D image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
- replaceLabels
  
  public static final void replaceLabels(ij.ImageStack image, float[] labels, float newLabel)
  
  Replace all values specified in label array by the specified value.
  
  Parameters:
  
  image - a 3D label image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
- remapLabels
  
  public static final void remapLabels(ij.ImagePlus imagePlus)
  
  Ensures that the labels in the given label image range from 1 to Lmax.
  
  Parameters:
  
  imagePlus - the instance of ImagePlus containing the label image
- remapLabels
  
  public static final void remapLabels(ij.process.ImageProcessor image)
  
  Ensures that the labels in the given label image range from 1 to Lmax.
  
  Parameters:
  
  image - the label image
- remapLabels
  
  public static final void remapLabels(ij.ImageStack image)
  
  Ensures that the labels in the given label image range from 1 to Lmax.
  
  Parameters:
  
  image - the 3D label image
- keepLabels
  
  public static final ij.ImagePlus keepLabels(ij.ImagePlus imagePlus, int[] labels)
  
  Creates a new image containing only the specified labels.
  
  Parameters:
  
  imagePlus - an ImagePlus containing a planar label image
  
  labels - the list of values to keep
  
  Returns:
  
  a new instance of ImagePlus containing only the specified labels
- keepLabels
  
  public static final ij.process.ImageProcessor keepLabels(ij.process.ImageProcessor image, int[] labels)
  
  Creates a new image containing only the specified labels.
  
  Parameters:
  
  image - a planar label image
  
  labels - the list of values to keep
  
  Returns:
  
  a new label image containing only the specified labels
- keepLabels
  
  public static final ij.ImageStack keepLabels(ij.ImageStack image, int[] labels)
  
  Creates a new image containing only the specified labels.
  
  Parameters:
  
  image - a 3D label image
  
  labels - the list of values to keep
  
  Returns:
  
  a new 3D label image containing only the specified labels
- applyLut
  
  public static final ij.ImagePlus applyLut(ij.ImagePlus labelImage, double[] values)
  
  Applies the given Look-up table to the input label image.
  
  Parameters:
  
  labelImage - a label image (2D or 3D)
  
  values - a set of values associated to each unique label
  
  Returns:
  
  a new ImagePlus containing for each pixel or voxel, either the value associated to the corresponding label, or 0 if the pixel is background
- applyLut
  
  public static final ij.process.FloatProcessor applyLut(ij.process.ImageProcessor labelImage, double[] values)
  
  Applies the given Look-up table to the input 2D label image.
  
  Parameters:
  
  labelImage - a label image
  
  values - a set of values associated to each unique label
  
  Returns:
  
  a new FloatProcessor containing for each pixel, either the value associated to the corresponding label, or 0 if the pixel is background
- applyLut
  
  public static final ij.ImageStack applyLut(ij.ImageStack labelImage, double[] values)
  
  Applies the given Look-up table to the input 3D label image.
  
  Parameters:
  
  labelImage - a 3D label image
  
  values - a set of values associated to each unique label
  
  Returns:
  
  a new 3D image containing for each pixel, either the value associated to the corresponding label, or 0 if the voxel is background
- mapLabelIndices
  
  public static final java.util.HashMap<java.lang.Integer,java.lang.Integer> mapLabelIndices(int[] labels)
  Creates an associative array to retrieve the index corresponding to each label.
  The resulting map verifies the relation:
  int[] labels = ... Map<Integer,Integer> labelIndices = LabelImages.mapLabelIndices(labels); int index = ... assert(index == labelIndices.get(labels[index]));
  Parameters:
  
  labels - an array of labels
  
  Returns:
  
  a HashMap instance with each label as key, and the index of the label in array as value.
- regionComponentsLabeling
  
  public static final ij.ImagePlus regionComponentsLabeling(ij.ImagePlus imagePlus, int regionLabel, int conn, int bitDepth)
  
  Computes the labels in the binary 2D or 3D image contained in the given ImagePlus, and computes the maximum label to set up the display range of the resulting ImagePlus.
  
  Parameters:
  
  imagePlus - contains the 3D binary image stack
  
  regionLabel - the label of the region to process, that can be the background (value 0)
  
  conn - the connectivity, either 4 or 8 for planar images, or 6 or 26 for 3D images
  
  bitDepth - the number of bits used to create the result image (8, 16 or 32)
  
  Returns:
  
  an ImagePlus containing the label of each connected component.
  
  Throws:
  
  java.lang.RuntimeException - if the number of labels reaches the maximum number that can be represented with this bitDepth
  
  See Also:
  
  FloodFillRegionComponentsLabeling, FloodFillRegionComponentsLabeling3D, FloodFill
- regionComponentsLabeling
  
  public static final ij.process.ImageProcessor regionComponentsLabeling(ij.process.ImageProcessor image, int regionLabel, int conn, int bitDepth)
  
  Computes the labels of the connected components in the given planar binary image. The type of result is controlled by the bitDepth option. Uses a Flood-fill type algorithm.
  
  Parameters:
  
  image - contains the binary image (any type is accepted)
  
  regionLabel - the label of the region to process, that can be the background (value 0)
  
  conn - the connectivity, either 4 or 8
  
  bitDepth - the number of bits used to create the result image (8, 16 or 32)
  
  Returns:
  
  a new instance of ImageProcessor containing the label of each connected component.
  
  Throws:
  
  java.lang.RuntimeException - if the number of labels reaches the maximum number that can be represented with this bitDepth
  
  See Also:
  
  FloodFillRegionComponentsLabeling, ConnectedComponentsLabeling
- regionComponentsLabeling
  
  public static final ij.ImageStack regionComponentsLabeling(ij.ImageStack image, int regionLabel, int conn, int bitDepth)
  
  Computes the labels of the connected components in the given 3D binary image. The type of result is controlled by the bitDepth option. Uses a Flood-fill type algorithm.
  
  Parameters:
  
  image - contains the 3D binary image (any type is accepted)
  
  regionLabel - the label of the region to process, that can be the background (value 0)
  
  conn - the connectivity, either 6 or 26
  
  bitDepth - the number of bits used to create the result stack (8, 16 or 32)
  
  Returns:
  
  a new instance of ImageStack containing the label of each connected component.
  
  Throws:
  
  java.lang.RuntimeException - if the number of labels reaches the maximum number that can be represented with this bitDepth
  
  See Also:
  
  FloodFillRegionComponentsLabeling3D, ConnectedComponentsLabeling3D
- mergeLabels
  
  public static final void mergeLabels(ij.ImagePlus labelImage, ij.gui.Roi roi, boolean verbose)
  
  Merge labels selected by freehand or point tool. Labels are merged in place (i.e., the input image is modified). Zero-value label is never merged.
  
  Parameters:
  
  labelImage - label image to modify
  
  roi - selection indicating the labels to merge
  
  verbose - option to write in the log window the labels merged
- mergeLabelsWithGap
  
  public static final void mergeLabelsWithGap(ij.ImagePlus labelImage, ij.gui.Roi roi, int conn, boolean verbose)
  
  Merge labels selected by freehand or point tool. Labels are merged in place (i.e., the input image is modified). Zero-value label is merged only if its neighbors contains two different labels.
  
  Parameters:
  
  labelImage - label image to modify
  
  roi - selection indicating the labels to merge
  
  conn - the connectivity to check for neighbors of background labels
  
  verbose - option to write in the log window the labels merged
- mergeLabelsWithGap
  
  public static final void mergeLabelsWithGap(ij.ImagePlus imagePlus, float[] labels, float newLabel, int conn)
  
  Merge several regions identified by their label, filling the gap between former regions. Labels are merged in place (i.e., the input image is modified). The background pixels or voxels are merged only if they are neighbor of at least two regions to be merged, and of no other region.
  
  Parameters:
  
  imagePlus - an ImagePlus containing a 3D label image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
  
  conn - the connectivity to check neighbors of background pixels
- mergeLabelsWithGap
  
  public static final void mergeLabelsWithGap(ij.process.ImageProcessor image, float[] labels, float newLabel, int conn)
  
  Merge several regions identified by their label, filling the gap between former regions. Labels are merged in place (i.e., the input image is modified). The background pixels are merged only if they are neighbor of at least two regions to be merged, and of no other region.
  
  Parameters:
  
  image - a label planar image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
  
  conn - the connectivity to check neighbors of background pixels
- mergeLabelsWithGap
  
  public static final void mergeLabelsWithGap(ij.ImageStack image, float[] labels, float newLabel, int conn)
  
  Merge several regions identified by their label, filling the gap between former regions. Labels are merged in place (i.e., the input image is modified). The background voxels are merged only if they are neighbor of at least two regions to be merged, and of no other region.
  
  Parameters:
  
  image - a 3D label image
  
  labels - the list of labels to replace
  
  newLabel - the new value for labels
  
  conn - the connectivity to check neighbors of background pixels
- removeLabels
  
  public static final void removeLabels(ij.ImagePlus labelImage, ij.gui.Roi roi, boolean verbose)
  
  Remove labels selected by freehand or point ROIs (in place).
  
  Parameters:
  
  labelImage - input label image
  
  roi - FreehandRoi or PointRoi with selected labels
  
  verbose - flag to print deleted labels in log window
- distanceMap
  
  public static final ij.process.ImageProcessor distanceMap(ij.process.ImageProcessor image)
  
  Computes the distance map for each region within a label map. Distance is computed for each label pixel, as the chamfer distance to the nearest pixel with a different value.
  
  Parameters:
  
  image - the input label image
  
  Returns:
  
  the distance map obtained after applying the distance transform
- distanceMap
  
  public static final ij.process.ImageProcessor distanceMap(ij.process.ImageProcessor image, ChamferMask2D mask, boolean floatingPoint, boolean normalize)
  
  Computes the distance map for each region within a label map, by specifying the chamfer mask and the normalization.
  
  Distance is computed for each foreground (white) pixel, as the chamfer distance to the nearest background (black) pixel. Result is given in a new instance of FloatProcessor or ShortProcessort, depending on the floatingPoint flag.
  
  Parameters:
  
  image - the input binary or label image
  
  mask - the chamfer mask used to propagate distances
  
  floatingPoint - indicates if the computation should be performed using floating point computation
  
  normalize - indicates whether the resulting distance map should be normalized (divide distances by the first chamfer weight)
  
  Returns:
  
  the distance map obtained after applying the distance transform
- distanceMap
  
  public static final ij.process.ImageProcessor distanceMap(ij.process.ImageProcessor image, short[] weights, boolean normalize)
  
  Computes the distance map for each region within a label map. Distance is computed for each label pixel, as the chamfer distance to the nearest pixel with a different value.
  
  Parameters:
  
  image - the input image of labels
  
  weights - an array of chamfer weights, with at least three values
  
  normalize - indicates whether the resulting distance map should be normalized (divide distances by the first chamfer weight)
  
  Returns:
  
  the distance map obtained after applying the distance transform
- distanceMap
  
  public static final ij.process.ImageProcessor distanceMap(ij.process.ImageProcessor image, float[] weights, boolean normalize)
  
  Computes the distance map from a 3D image of labels Distance is computed for each label voxel, as the chamfer distance to the nearest voxel with a different value.
  
  Parameters:
  
  image - the input 3D image of labels
  
  weights - an array of chamfer weights, with at least three values
  
  normalize - indicates whether the resulting distance map should be normalized (divide distances by the first chamfer weight)
  
  Returns:
  
  the distance map obtained after applying the distance transform
- distanceMap
  
  public static final ij.ImageStack distanceMap(ij.ImageStack image)
  
  Computes the 3D distance map from an image of labels. Distance is computed for each label voxel, as the chamfer distance to the nearest voxel with a different value.
  
  Parameters:
  
  image - the input 3D label image
  
  Returns:
  
  the distance map obtained after applying the distance transform
- distanceMap
  
  public static final ij.ImageStack distanceMap(ij.ImageStack image, ChamferMask3D mask, boolean floatingPoint, boolean normalize)
  
  Computes the 3D distance map from an image of labels, by specifying the chamfer mask and the normalization.
  
  Distance is computed for each foreground (white) pixel, as the chamfer distance to the nearest background (black) pixel. Result is given in a new instance of FloatProcessor.
  
  Parameters:
  
  image - the input 3D binary or label image
  
  mask - the chamfer mask used to propagate distances
  
  floatingPoint - indicates if the computation should be performed using floating point computation
  
  normalize - indicates whether the resulting distance map should be normalized (divide distances by the first chamfer weight)
  
  Returns:
  
  the distance map obtained after applying the distance transform
- distanceMap
  
  public static final ij.ImageStack distanceMap(ij.ImageStack image, short[] weights, boolean normalize)
  
  Computes the distance map from a 3D image of labels Distance is computed for each label voxel, as the chamfer distance to the nearest voxel with a different value.
  
  Parameters:
  
  image - the input 3D image of labels
  
  weights - an array of chamfer weights, with at least three values
  
  normalize - indicates whether the resulting distance map should be normalized (divide distances by the first chamfer weight)
  
  Returns:
  
  the distance map obtained after applying the distance transform
- distanceMap
  
  public static final ij.ImageStack distanceMap(ij.ImageStack image, float[] weights, boolean normalize)
  
  Computes the distance map from a 3D image of labels Distance is computed for each label voxel, as the chamfer distance to the nearest voxel with a different value.
  
  Parameters:
  
  image - the input 3D image of labels
  
  weights - an array of chamfer weights, with at least three values
  
  normalize - indicates whether the resulting distance map should be normalized (divide distances by the first chamfer weight)
  
  Returns:
  
  the distance map obtained after applying the distance transform
- dilateLabels
  
  public static final ij.ImagePlus dilateLabels(ij.ImagePlus imagePlus, double distMax)
  Applies a constrained dilation to each region in the 3D label map:
  
  The dilation of each region is constrained by the other regions;
  
  The dilation extent is limited by the specified distance (in voxel unit)
  Parameters:
  
  imagePlus - the image of regions to process
  
  distMax - the maximum distance used for dilation
  
  Returns:
  
  a new label map of regions, where regions are dilated only over the background.
- dilateLabels
  
  public static final ij.process.ImageProcessor dilateLabels(ij.process.ImageProcessor labelMap, double radius)
  Applies a constrained dilation to each region in the label map:
  
  The dilation of each region is constrained by the other regions;
  
  The dilation extent is given by a radius value (in pixel unit). In practice, the distance to radius+0.5 is computed.
  Parameters:
  
  labelMap - the image of regions to process
  
  radius - the radius of dilation of labels over the background
  
  Returns:
  
  a new label map of regions, where regions are dilated only over the background.
- dilateLabels
  
  public static final ij.ImageStack dilateLabels(ij.ImageStack labelMap, double radius)
  Applies a constrained dilation to each region in the 3D label map:
  
  The dilation of each region is constrained by the other regions;
  
  The dilation extent is limited by the the radius value (in voxel unit). In practice, the distance to radius+0.5 is computed.
  Parameters:
  
  labelMap - the image of regions to process
  
  radius - the radius of dilation of labels over the background
  
  Returns:
  
  a new label map of regions, where regions are dilated only over the background.
- getSelectedLabels
  
  public static final java.util.ArrayList<java.lang.Float> getSelectedLabels(ij.ImagePlus labelImage, ij.gui.Roi roi)
  
  Get list of selected labels in label image. Labels are selected by any selection tool in the current slice of the input label image. Zero-value label is skipped.
  
  Parameters:
  
  labelImage - label image
  
  roi - FreehandRoi or PointRoi with selected labels
  
  Returns:
  
  list of selected labels
- getTotalOverlap
  
  public static final double getTotalOverlap(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)
  
  Get the total overlap between two label images (source and target).
  Total Overlap (for all regions) $TO = \frac{ \sum_r{|S_r \cap T_r|} }{ \sum_r{|T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  total overlap value or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getTargetOverlapPerLabel
  
  public static final ij.measure.ResultsTable getTargetOverlapPerLabel(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)
  
  Get the target overlap between two label images (source and target) per each individual labeled region.
  Target Overlap (for individual label regions r) $TO_r = \frac{ |S_r \cap T_r| }{ |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  target overlap per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getTotalOverlap
  
  public static final double getTotalOverlap(ij.ImageStack sourceImage, ij.ImageStack targetImage)
  
  Get the total overlap between two label images (source and target).
  Total Overlap (for all regions) $TO = \frac{ \sum_r{|S_r \cap T_r|} }{ \sum_r{|T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  total overlap value or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getTotalOverlap
  
  public static final double getTotalOverlap(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)
  
  Get the total overlap between two label images (source and target).
  Total Overlap (for all regions) $TO = \frac{ \sum_r{|S_r \cap T_r|} }{ \sum_r{|T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  total overlap value or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getTargetOverlapPerLabel
  
  public static final ij.measure.ResultsTable getTargetOverlapPerLabel(ij.ImageStack sourceImage, ij.ImageStack targetImage)
  
  Get the target overlap between two label images (source and target) per each individual labeled region.
  Target Overlap (for individual label regions r) $TO_r = \frac{ |S_r \cap T_r| }{ |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  target overlap per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getTargetOverlapPerLabel
  
  public static final ij.measure.ResultsTable getTargetOverlapPerLabel(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)
  
  Get the target overlap between two label images (source and target) per each individual labeled region.
  Target Overlap (for individual label regions r) $TO_r = \frac{ |S_r \cap T_r| }{ |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  target overlap per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getJaccardIndex
  
  public static final double getJaccardIndex(ij.process.ImageProcessor labelImage1, ij.process.ImageProcessor labelImage2)
  
  Get the Jaccard index (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - first label image
  
  labelImage2 - second label image
  
  Returns:
  
  Jaccard index value or -1 if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/Jaccard_index
- getJaccardIndexPerLabel
  
  public static final ij.measure.ResultsTable getJaccardIndexPerLabel(ij.process.ImageProcessor labelImage1, ij.process.ImageProcessor labelImage2)
  
  Get the Jaccard index per label (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - reference label image
  
  labelImage2 - label image to compare with
  
  Returns:
  
  Jaccard index per label in the reference image or null if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/Jaccard_index
- getJaccardIndex
  
  public static final double getJaccardIndex(ij.ImageStack labelImage1, ij.ImageStack labelImage2)
  
  Get the Jaccard index (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - first label image
  
  labelImage2 - second label image
  
  Returns:
  
  Jaccard index value or -1 if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/Jaccard_index
- getJaccardIndexPerLabel
  
  public static final ij.measure.ResultsTable getJaccardIndexPerLabel(ij.ImageStack labelImage1, ij.ImageStack labelImage2)
  
  Get the Jaccard index per label (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - reference label image
  
  labelImage2 - label image to compare with
  
  Returns:
  
  Jaccard index per label in the reference image or null if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/Jaccard_index
- getJaccardIndex
  
  public static final double getJaccardIndex(ij.ImagePlus labelImage1, ij.ImagePlus labelImage2)
  
  Get the Jaccard index (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - first label image
  
  labelImage2 - second label image
  
  Returns:
  
  Jaccard index value or -1 if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/Jaccard_index
- getJaccardIndexPerLabel
  
  public static final ij.measure.ResultsTable getJaccardIndexPerLabel(ij.ImagePlus labelImage1, ij.ImagePlus labelImage2)
  
  Get the Jaccard index per label (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - reference label image
  
  labelImage2 - label image to compare with
  
  Returns:
  
  Jaccard index per label in the reference image or null if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/Jaccard_index
- getDiceCoefficient
  
  public static final double getDiceCoefficient(ij.process.ImageProcessor labelImage1, ij.process.ImageProcessor labelImage2)
  
  Get the Dice coefficient between two label images.
  
  Parameters:
  
  labelImage1 - first label image
  
  labelImage2 - second label image
  
  Returns:
  
  Dice coefficient value or -1 if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
- getDiceCoefficientPerLabel
  
  public static final ij.measure.ResultsTable getDiceCoefficientPerLabel(ij.process.ImageProcessor labelImage1, ij.process.ImageProcessor labelImage2)
  
  Get the Dice coefficient per label (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - reference label image
  
  labelImage2 - label image to compare with
  
  Returns:
  
  Dice coefficient per label in the reference image or null if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
- getDiceCoefficient
  
  public static final double getDiceCoefficient(ij.ImageStack labelImage1, ij.ImageStack labelImage2)
  
  Get the Dice coefficient between two label images.
  
  Parameters:
  
  labelImage1 - first label image
  
  labelImage2 - second label image
  
  Returns:
  
  Dice coefficient value or -1 if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
- getDiceCoefficientPerLabel
  
  public static final ij.measure.ResultsTable getDiceCoefficientPerLabel(ij.ImageStack labelImage1, ij.ImageStack labelImage2)
  
  Get the Dice coefficient per label (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - reference label image
  
  labelImage2 - label image to compare with
  
  Returns:
  
  Dice coefficient per label in the reference image or null if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
- getDiceCoefficient
  
  public static final double getDiceCoefficient(ij.ImagePlus labelImage1, ij.ImagePlus labelImage2)
  
  Get the Dice coefficient between two label images.
  
  Parameters:
  
  labelImage1 - first label image
  
  labelImage2 - second label image
  
  Returns:
  
  Dice coefficient value or -1 if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
- getDiceCoefficientPerLabel
  
  public static final ij.measure.ResultsTable getDiceCoefficientPerLabel(ij.ImagePlus labelImage1, ij.ImagePlus labelImage2)
  
  Get the Dice coefficient per label (intersection over union overlap) between two label images.
  
  Parameters:
  
  labelImage1 - reference label image
  
  labelImage2 - label image to compare with
  
  Returns:
  
  Dice coefficient per label in the reference image or null if error
  
  See Also:
  
  https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
- getVolumeSimilarity
  
  public static final double getVolumeSimilarity(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)
  
  Get the total volume similarity between two label images (source and target).
  Volume Similarity (for all regions) $VS = 2\frac{ \sum_r{|S_r| - |T_r|} }{ \sum_r{|S_r| + |T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  total volume similarity value
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getVolumeSimilarityPerLabel
  
  public static final ij.measure.ResultsTable getVolumeSimilarityPerLabel(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)
  
  Get the volume similarity between two label images (source and target) per each individual labeled region.
  Volume Similarity (for each label region r) $VS_r = 2\frac{ |S_r| - |T_r| }{ |S_r| + |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  volume similarity per label
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getVolumeSimilarity
  
  public static final double getVolumeSimilarity(ij.ImageStack sourceImage, ij.ImageStack targetImage)
  
  Get the total volume similarity between two label images (source and target).
  Volume Similarity (for all regions) $VS = 2\frac{ \sum_r{|S_r| - |T_r|} }{ \sum_r{|S_r| + |T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  total volume similarity value or NaN if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getVolumeSimilarityPerLabel
  
  public static final ij.measure.ResultsTable getVolumeSimilarityPerLabel(ij.ImageStack sourceImage, ij.ImageStack targetImage)
  
  Get the volume similarity between two label images (source and target) per each individual labeled region.
  Volume Similarity (for each label region r) $VS_r = 2\frac{ |S_r| - |T_r| }{ |S_r| + |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  volume similarity per label
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getVolumeSimilarity
  
  public static final double getVolumeSimilarity(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)
  
  Get the total volume similarity between two label images (source and target).
  Volume Similarity (for all regions) $VS = 2\frac{ \sum_r{|S_r| - |T_r|} }{ \sum_r{|S_r| + |T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  total volume similarity value
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getVolumeSimilarityPerLabel
  
  public static final ij.measure.ResultsTable getVolumeSimilarityPerLabel(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)
  
  Get the volume similarity between two label images (source and target) per each individual labeled region.
  Volume Similarity (for each label region r) $VS_r = 2\frac{ |S_r| - |T_r| }{ |S_r| + |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  volume similarity per label
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalseNegativeError
  
  public static final double getFalseNegativeError(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)
  
  Get the total false negative error between two label images (source and target).
  False Negative Error (for all regions) $FN = \frac{ \sum_r{|T_r \setminus S_r|} }{ \sum_r{|T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false negative error or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalseNegativeErrorPerLabel
  
  public static final ij.measure.ResultsTable getFalseNegativeErrorPerLabel(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)
  
  Get the false negative error between two label images (source and target) per each individual labeled region.
  False Negative Error (for each individual labeled region r) $FN_r = \frac{ |T_r \setminus S_r| }{ |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false negative error per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalseNegativeError
  
  public static final double getFalseNegativeError(ij.ImageStack sourceImage, ij.ImageStack targetImage)
  
  Get the total false negative error between two label images (source and target).
  False Negative Error (for all regions) $FN = \frac{ \sum_r{|T_r \setminus S_r|} }{ \sum_r{|T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false negative error or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalseNegativeErrorPerLabel
  
  public static final ij.measure.ResultsTable getFalseNegativeErrorPerLabel(ij.ImageStack sourceImage, ij.ImageStack targetImage)
  
  Get the false negative error between two label images (source and target) per each individual labeled region.
  False Negative Error (for each individual labeled region r) $FN_r = \frac{ |T_r \setminus S_r| }{ |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false negative error per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalseNegativeError
  
  public static final double getFalseNegativeError(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)
  
  Get the total false negative error between two label images (source and target).
  False Negative Error (for all regions) $FN = \frac{ \sum_r{|T_r \setminus S_r|} }{ \sum_r{|T_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false negative error or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalseNegativeErrorPerLabel
  
  public static final ij.measure.ResultsTable getFalseNegativeErrorPerLabel(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)
  
  Get the false negative error between two label images (source and target) per each individual labeled region.
  False Negative Error (for each individual labeled region r) $FN_r = \frac{ |T_r \setminus S_r| }{ |T_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false negative error per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalsePositiveError
  
  public static final double getFalsePositiveError(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)
  
  Get the total false positive error between two label images (source and target).
  False Positive Error (for all regions) $FP = \frac{ \sum_r{|S_r \setminus T_r|} }{ \sum_r{|S_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false positive error or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalsePositiveErrorPerLabel
  
  public static final ij.measure.ResultsTable getFalsePositiveErrorPerLabel(ij.process.ImageProcessor sourceImage, ij.process.ImageProcessor targetImage)
  
  Get the false positive error between two label images (source and target) per each individual labeled region.
  False Positive Error (for each individual labeled region r) $FN_r = \frac{ |S_r \setminus T_r| }{ |S_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false positive error per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalsePositiveError
  
  public static final double getFalsePositiveError(ij.ImageStack sourceImage, ij.ImageStack targetImage)
  
  Get the total false positive error between two label images (source and target).
  False Positive Error (for all regions) $FP = \frac{ \sum_r{|S_r \setminus T_r|} }{ \sum_r{|S_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false positive error or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalsePositiveErrorPerLabel
  
  public static final ij.measure.ResultsTable getFalsePositiveErrorPerLabel(ij.ImageStack sourceImage, ij.ImageStack targetImage)
  
  Get the false positive error between two label images (source and target) per each individual labeled region.
  False Positive Error (for each individual labeled region r) $FN_r = \frac{ |S_r \setminus T_r| }{ |S_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false positive error per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalsePositiveError
  
  public static final double getFalsePositiveError(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)
  
  Get the total false positive error between two label images (source and target).
  False Positive Error (for all regions) $FP = \frac{ \sum_r{|S_r \setminus T_r|} }{ \sum_r{|S_r|} }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false positive error or -1 if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- getFalsePositiveErrorPerLabel
  
  public static final ij.measure.ResultsTable getFalsePositiveErrorPerLabel(ij.ImagePlus sourceImage, ij.ImagePlus targetImage)
  
  Get the false positive error between two label images (source and target) per each individual labeled region.
  False Positive Error (for each individual labeled region r) $FN_r = \frac{ |S_r \setminus T_r| }{ |S_r| }$.
  
  Parameters:
  
  sourceImage - source label image
  
  targetImage - target label image
  
  Returns:
  
  false positive error per label or null if error
  
  See Also:
  
  http://www.insight-journal.org/browse/publication/707
- findPositionOfMaxValues
  
  @Deprecated public static final java.awt.Point[] findPositionOfMaxValues(ij.process.ImageProcessor valueImage, ij.process.ImageProcessor labelImage, int[] labels)
  
  Deprecated.
  use LavelValues.findPositionOfMaxValues instead
  
  For each label, finds the position of the point belonging to label region defined by labelImage and with maximal value in intensity image valueImage.
  
  Parameters:
  
  valueImage - the intensity image containing values to compare
  
  labelImage - the intensity image containing label of each pixel
  
  labels - the list of labels in the label image
  
  Returns:
  
  the position of maximum value in intensity image for each label
- findPositionOfMinValues
  
  @Deprecated public static final java.awt.Point[] findPositionOfMinValues(ij.process.ImageProcessor valueImage, ij.process.ImageProcessor labelImage, int[] labels)
  
  Deprecated.
  use LavelValues.findPositionOfMinValues instead
  
  For each label, finds the position of the point belonging to label region defined by labelImage and with minimal value in intensity image valueImage.
  
  Parameters:
  
  valueImage - the intensity image containing values to compare
  
  labelImage - the intensity image containing label of each pixel
  
  labels - the list of labels in the label image
  
  Returns:
  
  the position of minimum value in intensity image for each label

Class LabelImages

Method Summary

Methods inherited from class java.lang.Object

Method Details

isLabelImageType

createLabelImage

createLabelStack

createLabelImage

labelBoundaries

labelBoundaries

binarize

cropLabel

cropLabel

cropLabel

sizeOpening

areaOpening

volumeOpening

labelToRgb

labelToRgb

labelToRgb

removeBorderLabels

removeBorderLabels

removeBorderLabels

keepLargestLabel

keepLargestLabel

keepLargestLabel

removeLargestLabel

removeLargestLabel

removeLargestLabel

pixelCount

pixelCount

voxelCount

findLargestLabel

findAllLabels

findAllLabels

findAllLabels

replaceLabels

replaceLabels

replaceLabels

replaceLabels

replaceLabels

replaceLabels

remapLabels

remapLabels

remapLabels

keepLabels

keepLabels

keepLabels

applyLut

applyLut

applyLut

mapLabelIndices

regionComponentsLabeling

regionComponentsLabeling

regionComponentsLabeling

mergeLabels

mergeLabelsWithGap

mergeLabelsWithGap

mergeLabelsWithGap

mergeLabelsWithGap

removeLabels

distanceMap

distanceMap

distanceMap

distanceMap

distanceMap

distanceMap

distanceMap

distanceMap

dilateLabels

dilateLabels

dilateLabels

getSelectedLabels

getTotalOverlap

getTargetOverlapPerLabel

getTotalOverlap

getTotalOverlap

getTargetOverlapPerLabel

getTargetOverlapPerLabel

getJaccardIndex