Class AlgorithmEvaluateMaskSegmentation

java.lang.Object
java.lang.Thread
gov.nih.mipav.model.algorithms.AlgorithmBase
gov.nih.mipav.model.algorithms.AlgorithmEvaluateMaskSegmentation
All Implemented Interfaces:
ActionListener, WindowListener, Runnable, EventListener

public class AlgorithmEvaluateMaskSegmentation extends AlgorithmBase
Compares segmentation results of a test image to segmentation results of an ideal gold standard true image. Comparisons are made for identical nonzero levels in the 2 images. For each nonzero level, the false negative volume fraction, the false positive volume fraction, and the positive volume fraction are output to the global data text. The images must be boolean, UBYTE, or USHORT.
  • Field Details

    • absoluteTrue

      private int absoluteTrue
      total number of voxels at a particular nonzero level.
    • falseNegative

      private int falseNegative
      where trueImage has the nonzero level.
    • falsePositive

      private int falsePositive
      where trueImage does not have the nonzero level.
    • fnvf

      private float fnvf
      false negative volume fraction.
    • fpvf

      private float fpvf
      false positive volume fraction.
    • length

      private int length
      DOCUMENT ME!
    • levelsMatch

      private int levelsMatch
      DOCUMENT ME!
    • present

      private boolean present
      DOCUMENT ME!
    • testArray

      private short[] testArray
      DOCUMENT ME!
    • testImage

      private ModelImage testImage
      DOCUMENT ME!
    • testLength

      private int testLength
      DOCUMENT ME!
    • testLevelArray

      private int[] testLevelArray
      DOCUMENT ME!
    • testLevels

      private int testLevels
      DOCUMENT ME!
    • tpvf

      private float tpvf
      positive volume fraction.
    • trueArray

      private short[] trueArray
      DOCUMENT ME!
    • trueFound

      private int trueFound
      in the true image.
    • trueLevelArray

      private int[] trueLevelArray
      DOCUMENT ME!
    • trueLevels

      private int trueLevels
      DOCUMENT ME!
  • Constructor Details

    • AlgorithmEvaluateMaskSegmentation

      public AlgorithmEvaluateMaskSegmentation(ModelImage trueImage, ModelImage testImage)
      Creates a new AlgorithmEvaluateMaskSegmentation object.
      Parameters:
      trueImage - image model used as a ideal gold standard
      testImage - image model tested against the trueImage
  • Method Details

    • finalize

      public void finalize()
      Prepares this class for destruction.
      Overrides:
      finalize in class AlgorithmBase
    • runAlgorithm

      public void runAlgorithm()
      Starts the program.
      Specified by:
      runAlgorithm in class AlgorithmBase