Package gov.nih.mipav.model.algorithms

Class AlgorithmVOIProps.Calc34D

java.lang.Object
gov.nih.mipav.model.algorithms.AlgorithmVOIProps.Calc34D
All Implemented Interfaces:
Runnable
Enclosing class:
AlgorithmVOIProps
private class AlgorithmVOIProps.Calc34D extends Object implements Runnable

  • Field Details

    • calcSelectedVOI

      private VOI calcSelectedVOI
      The VOI being used just for this calculation (may be a single curve of a single slice or entire VOI

  • Constructor Details

    • Calc34D

      public Calc34D(VOI selectedVOI)

  • Method Details

    • getMedianStatistics

      public void getMedianStatistics(AlgorithmVOIProps.Calc34D.ContourStats stats)
      Calculates the median statistics for a grayscale image. The image data is stored in the input stats.values. This function can be used for a single contour or a group of contours.
      Parameters:
      stats - contains the Vector of voxels inside the contour(s) used to calculate the median, mode and mode count.

    • getMedianStatisticsRGB

      public void getMedianStatisticsRGB(AlgorithmVOIProps.Calc34D.ContourStats stats)
      Calculates the median statistics for a color image. The image data is stored in the input stats.valuesRGB. This function can be used for a single contour or a group of contours.
      Parameters:
      stats - contains the Vector of voxels inside the contour(s) used to calculate the median, mode and mode count.

    • getStdSkewStatistics

      public void getStdSkewStatistics(AlgorithmVOIProps.Calc34D.ContourStats stats, Vector<WildMagic.LibFoundation.Mathematics.Vector3f> kPositions, String unit2DStr, String unit3DStr, float ignoreMin, float ignoreMax)
      Calculates the standard deviation, coefficient of skewness, coefficient of kurtosis and center of mass for a grayscale image.
      Parameters:
      stats - output values are written into stats.
      kPositions - list of positions of voxels inside the contour (or multiple contours).
      unit2DStr - , 2D label string
      unit3DStr - , 3D label string
      ignoreMin - , threshold minimum
      ignoreMax - , threshold maximum

    • getStdSkewStatisticsRGB

      public void getStdSkewStatisticsRGB(AlgorithmVOIProps.Calc34D.ContourStats stats, Vector<WildMagic.LibFoundation.Mathematics.Vector3f> kPositions, String unit2DStr, String unit3DStr, float ignoreMinR, float ignoreMaxR, float ignoreMinG, float ignoreMaxG, float ignoreMinB, float ignoreMaxB)
      Calculates the standard deviation, coefficient of skewness, coefficient of kurtosis and center of mass for a color image.
      Parameters:
      stats - output values are written into stats.
      kPositions - list of positions of voxels inside the contour (or multiple contours).
      unit2DStr - , 2D label string
      unit3DStr - , 3D label string
      ignoreMinR - , threshold minimum red
      ignoreMaxR - , threshold maximum red
      ignoreMinG - , threshold minimum green
      ignoreMaxG - , threshold maximum green
      ignoreMinB - , threshold minimum blue
      ignoreMaxB - , threshold maximum blue

    • run

      public void run()
      Specified by:
      run in interface Runnable

    • calcStatsPerContour

      private AlgorithmVOIProps.Calc34D.ContourStats calcStatsPerContour(FileInfoBase fileInfo, VOIBase contour, int orientation, String unit2DStr, String unit3DStr, float ignoreMin, float ignoreMax, VOIStatisticalProperties statProperty, int iSlice, int iID)
      Calculates statistics for a single contour. Calculations are done on an as-needed basis for faster performance.
      Parameters:
      fileInfo - information on image resolutions and dimensions
      contour - input contour
      orientation - the image plane the contour is on
      unit2DStr - 2D label string
      unit3DStr - 3D label string
      ignoreMin - threshold minimum
      ignoreMax - threshold maximum
      statProperty - output labels are written to statProperty
      iSlice - contour slice
      iID - contour iD
      Returns:
      ContourStats data structure contains the statistic values.

    • calcStatsPerContourRGB

      private AlgorithmVOIProps.Calc34D.ContourStats calcStatsPerContourRGB(FileInfoBase fileInfo, VOIBase contour, int orientation, String unit2DStr, String unit3DStr, float ignoreMinR, float ignoreMaxR, float ignoreMinG, float ignoreMaxG, float ignoreMinB, float ignoreMaxB, VOIStatisticalProperties statProperty, int iSlice, int iID)
      Calculates statistics for a single contour. Calculations are done on an as-needed basis for faster performance.
      Parameters:
      fileInfo - information on image resolutions and dimensions
      contour - input contour
      orientation - the image plane the contour is on
      unit2DStr - 2D label string
      unit3DStr - 3D label string
      ignoreMinR - threshold minimum red
      ignoreMaxR - threshold maximum red
      ignoreMinG - threshold minimum green
      ignoreMaxG - threshold maximum green
      ignoreMinB - threshold minimum blue
      ignoreMaxB - threshold maximum blue
      statProperty - output labels are written to statProperty
      iSlice - contour slice
      iID - contour iD
      Returns:
      ContourStats data structure contains the statistic values.

    • calcStatsTotal

      private void calcStatsTotal(VOI kVOI, VOIStatisticalProperties statProperty, FileInfoBase fileInfo, String unit2DStr, String unit3DStr, float ignoreMin, float ignoreMax)
      Calculates the statistics for the VOI as a whole (across all contours within the VOI). Calculations are done on an as-needed basis for faster performance.
      Parameters:
      statProperty - stores the calculated output
      fileInfo - information for image resolutions
      unit2DStr - 2D label string
      unit3DStr - 3D label string
      ignoreMin - threshold minimum
      ignoreMax - threshold maximum
      contours - list of contours in the VOI
      largestDistance - largest Distance in the VOI. private void calcStatsTotal( Vector contours, VOIStatisticalProperties statProperty, FileInfoBase fileInfo, String unit2DStr, String unit3DStr, float ignoreMin, float ignoreMax, double largestDistance ) { ContourStats stats = new ContourStats(); // The following depend on each other: quantityDescription, volumeDescription, areaDescription, // circularityDescription, solidityDescription // If one is selected, all are calculated. if ( statsList[ indexOf( quantityDescription ) ] || statsList[ indexOf( volumeDescription ) ] || statsList[ indexOf( areaDescription ) ] || statsList[ indexOf( circularityDescription)] || statsList[ indexOf( solidityDescription)]) { stats.nVox = 0; stats.area = 0; stats.volume = 0; for ( int i = 0; i invalid input: '<' contours.size(); i++ ) { int nVox = contours.elementAt(i).getNumVoxels(); stats.nVox += nVox; stats.area += nVox * (fileInfo.getResolutions()[0] * fileInfo.getResolutions()[1]); stats.volume += nVox * (fileInfo.getResolutions()[0] * fileInfo.getResolutions()[1] * fileInfo.getResolutions()[2]); } statProperty.setProperty(VOIStatisticList.areaDescription, nf.format(stats.area)); statProperty.setProperty(VOIStatisticList.volumeDescription, nf.format(stats.volume)); statProperty.setProperty(VOIStatisticList.quantityDescription, nf.format(stats.nVox)); } // If user selects perimeterDescription or circularityDescription: if ( statsList[ indexOf( perimeterDescription ) ] || statsList [ indexOf (circularityDescription) ]) { stats.perimeter = 0; for ( int i = 0; i invalid input: '<' contours.size(); i++ ) { stats.perimeter += contours.elementAt(i).getLengthPtToPt(srcImage.getFileInfo(0).getResolutions()); } statProperty.setProperty(VOIStatisticList.perimeterDescription, nf.format(stats.perimeter)); } if ( statsList[indexOf(circularityDescription)]) { stats.circularity = (4.0 * Math.PI * stats.area)/(stats.perimeter * stats.perimeter); statProperty.setProperty(VOIStatisticList.circularityDescription + end, nf.format(stats.circularity)); } if ( statsList[indexOf(solidityDescription)]) { VOI convexKVOI = (VOI)kVOI.clone(); for ( int i = 0; i invalid input: '<' convexKVOI.getCurves().size(); i++) { ((VOIContour)convexKVOI.getCurves().elementAt(i)).convexHull(); } ContourStats convexStats = new ContourStats(); BitSet convexMask = new BitSet( xDim * yDim * zDim ); convexKVOI.createBinaryMask3D(convexMask, xDim, yDim, false, false); if(rangeFlag != RangeType.NO_RANGE invalid input: '&'invalid input: '&' rangeFlag != null) { //some intensity values need to be ignored in relevant calculations float fVal = 0.0f; for (int i = convexMask.nextSetBit(0); i >= 0; i = convexMask.nextSetBit(i+1)) { fVal = srcImage.getFloat(i); if (MipavUtil.inRange(ignoreMin, ignoreMax, fVal, rangeFlag)) { convexMask.set(i, false); } } } convexStats.nVox = convexMask.cardinality(); convexStats.area = convexStats.nVox * (fileInfo.getResolutions()[0] * fileInfo.getResolutions()[1]); stats.solidity = stats.area/convexStats.area; statProperty.setProperty(VOIStatisticList.solidityDescription, nf.format(stats.solidity)); } if (statsList[indexOf(numberOfIndentationsDescription)]) { stats.numberOfIndentations = contour.findVOIIndentations2D(srcImage, iSlice, null, false); statProperty.setProperty(VOIStatisticList.numberOfIndentationsDescription + end, nf.format(stats.numberOfIndentations)); } if (statsList[indexOf(meanCurvatureDescription)] || statsList[indexOf(stdDevCurvatureDescription)] || statsList[indexOf(meanNegativeCurvatureDescription)]) { Vector positions = new Vector(); Vector curvature = new Vector(); double meanCurvature[] = new double[1]; double stdDevCurvature[] = new double[1]; double meanNegativeCurvature[] = new double[1]; boolean smooth = true; VOIBase convexContour = new VOIContour((VOIContour)contour); convexContour.findPositionAndCurvature( srcImage, positions, curvature, smooth, meanCurvature, stdDevCurvature, meanNegativeCurvature); if (statsList[indexOf(meanCurvatureDescription)]) { stats.meanCurvature = meanCurvature[0]; statProperty.setProperty(VOIStatisticList.meanCurvatureDescription + end, nf.format(stats.meanCurvature)); } if (statsList[indexOf(stdDevCurvatureDescription)]) { stats.stdDevCurvature = stdDevCurvature[0]; statProperty.setProperty(VOIStatisticList.stdDevCurvatureDescription + end, nf.format(stats.stdDevCurvature)); } if (statsList[indexOf(meanNegativeCurvatureDescription)]) { stats.meanNegativeCurvature = meanNegativeCurvature[0]; statProperty.setProperty(VOIStatisticList.meanNegativeCurvatureDescription + end, nf.format(stats.meanNegativeCurvature)); } } // The following statistics are derived from the minIntensity, maxIntensity, avgIntensity, and sumIntensity: // median, mode, modeCount // deviationDescription, skewnessDescription, kurtosisDescription, massCenterDescription if ( statsList[ indexOf( minIntensity ) ] || statsList[ indexOf( maxIntensity ) ] || statsList[ indexOf( avgIntensity ) ] || statsList[ indexOf( sumIntensities ) ] || statsList[ indexOf( median ) ] || statsList[ indexOf( mode ) ] || statsList[ indexOf( modeCount ) ] || statsList[ indexOf( deviationDescription ) ] || statsList[ indexOf( skewnessDescription ) ] || statsList[ indexOf( kurtosisDescription ) ] || statsList[ indexOf( massCenterDescription ) ] ) { stats.nVox = 0; stats.values = new Vector(); stats.valuesRGB = new Vector(); for ( int i = 0; i invalid input: '<' contours.size(); i++ ) { int nVox = contours.elementAt(i).getNumVoxels(); stats.nVox += nVox; if (srcImage.isColorImage()) { ColorRGB kMin = new ColorRGB(); ColorRGB kMax = new ColorRGB(); ColorRGB kSum = new ColorRGB(); stats.valuesRGB.addAll( contours.elementAt(i).calcRGBIntensity( srcImage, kMin, kMax, kSum, ignoreMin, ignoreMax, rangeFlag ) ); if ( i == 0 ) { stats.minIntenRed = kMin.R; stats.minIntenGreen = kMin.G; stats.minIntenBlue = kMin.B; stats.maxIntenRed = kMax.R; stats.maxIntenGreen = kMax.G; stats.maxIntenBlue = kMax.B; } stats.minIntenRed = Math.min( stats.minIntenRed, kMin.R ); stats.minIntenGreen = Math.min( stats.minIntenGreen, kMin.G ); stats.minIntenBlue = Math.min( stats.minIntenBlue, kMin.B ); stats.maxIntenRed = Math.max( stats.minIntenRed, kMax.R ); stats.maxIntenGreen = Math.max( stats.minIntenGreen, kMax.G ); stats.maxIntenBlue = Math.max( stats.minIntenBlue, kMax.B ); stats.sumR += kSum.R; stats.sumG += kSum.G; stats.sumB += kSum.B; } else { Vector3f kMinMax = new Vector3f(); stats.values.addAll( contours.elementAt(i).calcIntensity( srcImage, kMinMax, ignoreMin, ignoreMax, rangeFlag ) ); if ( i == 0 ) { stats.minIntensity = kMinMax.X; stats.maxIntensity = kMinMax.Y; } stats.minIntensity = Math.min( stats.minIntensity, kMinMax.X ); stats.maxIntensity = Math.max( stats.maxIntensity, kMinMax.Y ); stats.sum += kMinMax.Z; } } statProperty.setProperty(VOIStatisticList.quantityDescription, nf.format(stats.nVox)); if (srcImage.isColorImage()) { stats.avgIntenR = stats.sumR/stats.nVox; stats.avgIntenG = stats.sumG/stats.nVox; stats.avgIntenB = stats.sumB/stats.nVox; statProperty.setProperty(VOIStatisticList.minIntensity + "Red", nf.format(stats.minIntenRed)); statProperty.setProperty(VOIStatisticList.maxIntensity + "Red", nf.format(stats.maxIntenRed)); statProperty.setProperty(VOIStatisticList.minIntensity + "Green", nf.format(stats.minIntenGreen)); statProperty.setProperty(VOIStatisticList.maxIntensity + "Green", nf.format(stats.maxIntenGreen)); statProperty.setProperty(VOIStatisticList.minIntensity + "Blue", nf.format(stats.minIntenBlue)); statProperty.setProperty(VOIStatisticList.maxIntensity + "Blue", nf.format(stats.maxIntenBlue)); statProperty.setProperty(VOIStatisticList.avgIntensity + "Red", nf.format(stats.avgIntenR)); statProperty.setProperty(VOIStatisticList.avgIntensity + "Green", nf.format(stats.avgIntenG)); statProperty.setProperty(VOIStatisticList.avgIntensity + "Blue", nf.format(stats.avgIntenB)); statProperty.setProperty(VOIStatisticList.sumIntensities + "Red", nf.format(stats.sumR)); statProperty.setProperty(VOIStatisticList.sumIntensities + "Green", nf.format(stats.sumG)); statProperty.setProperty(VOIStatisticList.sumIntensities + "Blue", nf.format(stats.sumB)); } else { stats.avgInten = stats.sum/stats.nVox; statProperty.setProperty(VOIStatisticList.minIntensity, nf.format(stats.minIntensity)); statProperty.setProperty(VOIStatisticList.maxIntensity, nf.format(stats.maxIntensity)); statProperty.setProperty(VOIStatisticList.avgIntensity, nf.format(stats.avgInten)); statProperty.setProperty(VOIStatisticList.sumIntensities, nf.format(stats.sum)); } // median, mode, modeCount if ( statsList[ indexOf( median ) ] || statsList[ indexOf( mode ) ] || statsList[ indexOf( modeCount ) ] ) { if (srcImage.isColorImage()) { getMedianStatisticsRGB( stats ); statProperty.setProperty(VOIStatisticList.mode + "Red", nf.format(stats.modeR)); statProperty.setProperty(VOIStatisticList.mode + "Green", nf.format(stats.modeG)); statProperty.setProperty(VOIStatisticList.mode + "Blue", nf.format(stats.modeB)); statProperty.setProperty(VOIStatisticList.modeCount + "Red", nf.format(stats.maxCountR)); statProperty.setProperty(VOIStatisticList.modeCount + "Green", nf.format(stats.maxCountG)); statProperty.setProperty(VOIStatisticList.modeCount + "Blue", nf.format(stats.maxCountB)); statProperty.setProperty(VOIStatisticList.median + "Red", nf.format(stats.medianR)); statProperty.setProperty(VOIStatisticList.median + "Green", nf.format(stats.medianG)); statProperty.setProperty(VOIStatisticList.median + "Blue", nf.format(stats.medianB)); } else { getMedianStatistics( stats ); statProperty.setProperty(VOIStatisticList.mode, nf.format(stats.mode)); statProperty.setProperty(VOIStatisticList.modeCount, nf.format(stats.maxCount)); statProperty.setProperty(VOIStatisticList.median, nf.format(stats.median)); } } // deviationDescription, skewnessDescription, kurtosisDescription, massCenterDescription if ( statsList[ indexOf( deviationDescription ) ] || statsList[ indexOf( skewnessDescription ) ] || statsList[ indexOf( kurtosisDescription ) ] || statsList[ indexOf( massCenterDescription ) ] ) { Vector kPositions = new Vector(); for ( int i = 0; i invalid input: '<' contours.size(); i++ ) { kPositions.addAll( contours.elementAt(i).getMaskPositions() ); } if (srcImage.isColorImage()) { getStdSkewStatisticsRGB(stats, kPositions, unit2DStr, unit3DStr, ignoreMin, ignoreMax); statProperty.setProperty(VOIStatisticList.deviationDescription + "Red", nf.format(stats.stdDevR)); statProperty.setProperty(VOIStatisticList.deviationDescription + "Green", nf.format(stats.stdDevG)); statProperty.setProperty(VOIStatisticList.deviationDescription + "Blue", nf.format(stats.stdDevB)); statProperty.setProperty(VOIStatisticList.skewnessDescription + "Red", nf.format(stats.skewnessR)); statProperty.setProperty(VOIStatisticList.skewnessDescription + "Green", nf.format(stats.skewnessG)); statProperty.setProperty(VOIStatisticList.skewnessDescription + "Blue", nf.format(stats.skewnessB)); statProperty.setProperty(VOIStatisticList.kurtosisDescription + "Red", nf.format(stats.kurtosisR)); statProperty.setProperty(VOIStatisticList.kurtosisDescription + "Green", nf.format(stats.kurtosisG)); statProperty.setProperty(VOIStatisticList.kurtosisDescription + "Blue", nf.format(stats.kurtosisB)); statProperty.setProperty(VOIStatisticList.massCenterDescription + "Red", stats.massCenterDescriptionR); statProperty.setProperty(VOIStatisticList.massCenterDescription + "Green", stats.massCenterDescriptionG); statProperty.setProperty(VOIStatisticList.massCenterDescription + "Blue", stats.massCenterDescriptionB); } else { getStdSkewStatistics(stats, kPositions, unit2DStr, unit3DStr, ignoreMin, ignoreMax); statProperty.setProperty(VOIStatisticList.deviationDescription, nf.format(stats.stdDev)); statProperty.setProperty(VOIStatisticList.skewnessDescription, nf.format(stats.skewness)); statProperty.setProperty(VOIStatisticList.kurtosisDescription , nf.format(stats.kurtosis)); statProperty.setProperty(VOIStatisticList.massCenterDescription, stats.massCenterDescription); } } } // If user selects geometricCenterDescription if ( statsList[ indexOf( geometricCenterDescription ) ] ) { Vector3f selectedCOM = calcSelectedVOI.getGeometricCenter(); selectedCOM.X *= srcImage.getFileInfo(0).getResolutions()[0]; selectedCOM.Y *= srcImage.getFileInfo(0).getResolutions()[1]; String unitStr = unit2DStr + "\tZ"; if (srcImage.getNDims() > 2) { selectedCOM.Z *= srcImage.getFileInfo(0).getResolutions()[2]; unitStr = unit3DStr; } String comStr = unitStr + "\n\t\t" + nf.format(selectedCOM.X) + "\t" + nf.format(selectedCOM.Y) + "\t" + nf.format(selectedCOM.Z); comStr = addScannerLabels(comStr, selectedCOM); stats.gCenterString = new String(comStr); statProperty.setProperty(VOIStatisticList.geometricCenterDescription, stats.gCenterString); } // If user selects largestSliceDistanceDescription: if ( statsList[ indexOf( largestSliceDistanceDescription ) ] ) { for ( int i = 0; i invalid input: '<' contours.size(); i++ ) { Vector3f kPos1 = new Vector3f(); Vector3f kPos2 = new Vector3f(); if ( i == 0 ) { stats.largestContourDistance = ((VOIContour) (contours.elementAt(i))).calcLargestSliceDistance( fileInfo.getResolutions()[0], fileInfo.getResolutions()[1], kPos1, kPos2); // Uncomment the following to at the VOILine to the image: //VOILine kLine = new VOILine(); //kLine.add(kPos1); //kLine.add(kPos2); //VOI kVOI = new VOI( (short)srcImage.getVOIs().size(), "LargestDist", VOI.LINE, 0 ); //kVOI.importCurve(kLine); //srcImage.registerVOI(kVOI); } else { stats.largestContourDistance = Math.max( stats.largestContourDistance, ((VOIContour) (contours.elementAt(i))).calcLargestSliceDistance( fileInfo.getResolutions()[0], fileInfo.getResolutions()[1], kPos1, kPos2)); // Uncomment the following to at the VOILine to the image: //VOILine kLine = new VOILine(); //kLine.add(kPos1); //kLine.add(kPos2); //VOI kVOI = new VOI( (short)srcImage.getVOIs().size(), "LargestDist", VOI.LINE, 0 ); //kVOI.importCurve(kLine); //srcImage.registerVOI(kVOI); } } statProperty.setProperty(VOIStatisticList.largestSliceDistanceDescription, nf.format(stats.largestContourDistance)); } if ( statsList[ indexOf( largestDistanceDescription ) ] ) { stats.largestContourDistance = largestDistance; statProperty.setProperty(VOIStatisticList.largestDistanceDescription, nf.format(stats.largestContourDistance)); } // Ellipsoid fit parameters are calculated as a group, if one is selected, calculate all. // For the VOI, only calculate these parameters for one contour: if ( statsList[ indexOf( axisDescription ) ] || statsList[ indexOf( eccentricityDescription ) ] || statsList[ indexOf( majorAxisDescription ) ] || statsList[ indexOf( minorAxisDescription ) ] ) { float[] tmpPAxis = new float[1]; float[] tmpEcc = new float[1]; float[] tmpMajorAxis = new float[1]; float[] tmpMinorAxis = new float[1]; ((VOIContour)contours.firstElement()).secondOrderAttributes(srcImage, tmpPAxis, tmpEcc, tmpMajorAxis, tmpMinorAxis); stats.PAxis = tmpPAxis[0]; stats.Ecc = tmpEcc[0]; stats.MajorAxis = tmpMajorAxis[0]; stats.MinorAxis = tmpMinorAxis[0]; System.out.println("HERE2"); statProperty.setProperty(VOIStatisticList.axisDescription, nf.format(stats.PAxis)); statProperty.setProperty(VOIStatisticList.eccentricityDescription, nf.format(stats.Ecc)); statProperty.setProperty(VOIStatisticList.majorAxisDescription, nf.format(stats.MajorAxis)); statProperty.setProperty(VOIStatisticList.minorAxisDescription, nf.format(stats.MinorAxis)); } }

    • calcStatsTotalRGB

      private void calcStatsTotalRGB(VOI kVOI, VOIStatisticalProperties statProperty, FileInfoBase fileInfo, String unit2DStr, String unit3DStr, float ignoreMinR, float ignoreMaxR, float ignoreMinG, float ignoreMaxG, float ignoreMinB, float ignoreMaxB)

    • printTotals

      private void printTotals(AlgorithmVOIProps.Calc34D.ContourStats[] stats, VOIStatisticalProperties statProperty, String unit2DStr, String unit3DStr, String end, float ignoreMin, float ignoreMax, double largestDistance)
      Sums the statistics for a group of contours, where the statistics are first calculated per-contour.
      Parameters:
      stats - array of per-contour statistics
      statProperty - output is written to the statProperty
      unit2DStr - 2D label string
      unit3DStr - 3D label string
      end - tag for statProperty labels
      ignoreMin - threshold minimum
      ignoreMax - threshold maximum
      largestDistance - VOI largest distance.

    • printTotalsRGB

      private void printTotalsRGB(AlgorithmVOIProps.Calc34D.ContourStats[] stats, VOIStatisticalProperties statProperty, String unit2DStr, String unit3DStr, String end, float ignoreMinR, float ignoreMaxR, float ignoreMinG, float ignoreMaxG, float ignoreMinB, float ignoreMaxB, double largestDistance)
      Sums the statistics for a group of contours, where the statistics are first calculated per-contour.
      Parameters:
      stats - array of per-contour statistics
      statProperty - output is written to the statProperty
      unit2DStr - 2D label string
      unit3DStr - 3D label string
      end - tag for statProperty labels
      ignoreMinR - threshold minimum red
      ignoreMaxR - threshold maximum red
      ignoreMinG - threshold minimum green
      ignoreMaxG - threshold maximum green
      ignoreMinB - threshold minimum blue
      ignoreMaxB - threshold maximum blue
      largestDistance - VOI largest distance.

    • printTotals

      private void printTotals(Vector<AlgorithmVOIProps.Calc34D.ContourStats> allStats, VOIStatisticalProperties statProperty, String unit2DStr, String unit3DStr, String end, float ignoreMin, float ignoreMax, double largestDistance)
      Sums the statistics for a group of contours, where the statistics are first calculated per-contour.
      Parameters:
      allStats - list of per-contour statistics
      statProperty - output is written to the statProperty
      unit2DStr - 2D label string
      unit3DStr - 3D label string
      end - tag for statProperty labels
      ignoreMin - threshold minimum
      ignoreMax - threshold maximum
      largestDistance - VOI largest distance.

    • printTotalsRGB

      private void printTotalsRGB(Vector<AlgorithmVOIProps.Calc34D.ContourStats> allStats, VOIStatisticalProperties statProperty, String unit2DStr, String unit3DStr, String end, float ignoreMinR, float ignoreMaxR, float ignoreMinG, float ignoreMaxG, float ignoreMinB, float ignoreMaxB, double largestDistance)
      Sums the statistics for a group of contours, where the statistics are first calculated per-contour.
      Parameters:
      allStats - list of per-contour statistics
      statProperty - output is written to the statProperty
      unit2DStr - 2D label string
      unit3DStr - 3D label string
      end - tag for statProperty labels
      ignoreMinR - threshold minimum red
      ignoreMaxR - threshold maximum red
      ignoreMinG - threshold minimum green
      ignoreMaxG - threshold maximum green
      ignoreMinB - threshold minimum blue
      ignoreMaxB - threshold maximum blue
      largestDistance - VOI largest distance.