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ijp-biop-run-macro/algorithms/MandersCorrelation.java
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MandersCorrelation.java
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package algorithms;
import results.ResultHandler;
import gadgets.DataContainer;
import imglib.mpicbg.imglib.cursor.special.TwinValueRangeCursor;
import imglib.mpicbg.imglib.cursor.special.meta.Predicate;
import imglib.mpicbg.imglib.cursor.special.meta.AboveThresholdPredicate;
import mpicbg.imglib.cursor.Cursor;
import mpicbg.imglib.image.Image;
import mpicbg.imglib.type.numeric.RealType;
/**
* This algorithm calculates Manders et al.'s two two correlation
* values M1 and M2. Those coefficients are independent from signal
* intensities, but are directly proportional to the amount of
* flourescence in the co-localized objects in each component of the
* image. See "Manders, Verbeek, Aten - Measurement of co-localization
* of objects in dual-colour confocal images".
*
* @param <T>
*/
public class MandersCorrelation<T extends RealType<T>> extends Algorithm<T> {
// Manders M1 and M2 value
double mandersM1, mandersM2;
// thresholded Manders M1 and M2 values
double mandersThresholdedM1, mandersThresholdedM2;
/**
* A result containter for Manders' calculations.
*/
public static class MandersResults {
public double m1;
public double m2;
}
@Override
public void execute(DataContainer<T> container)
throws MissingPreconditionException {
// get the two images for the calculation of Manders' values
Image<T> img1 = container.getSourceImage1();
Image<T> img2 = container.getSourceImage2();
// calculate Mander's values
MandersResults results = calculateMandersCorrelation(img1, img2);
// save the results
mandersM1 = results.m1;
mandersM2 = results.m2;
// calculate the thresholded values, if possible
AutoThresholdRegression<T> autoThreshold = container.getAutoThreshold();
if (autoThreshold != null ) {
Predicate<T> img1Predicate =
new AboveThresholdPredicate<T>( autoThreshold.getCh1MaxThreshold() );
Predicate<T> img2Predicate =
new AboveThresholdPredicate<T>( autoThreshold.getCh2MaxThreshold() );
TwinValueRangeCursor<T> cursor =
new TwinValueRangeCursor<T>(img1.createCursor(), img2.createCursor(), img1Predicate, img2Predicate);
// calculate Mander's values
results = calculateMandersCorrelation(cursor, container.getIntegralCh1(), container.getIntegralCh2() );
// save the results
mandersThresholdedM1 = results.m1;
mandersThresholdedM2 = results.m2;
}
}
/**
* Calculates Manders' split M1 and M2 values.
*
* @param img1 The first image
* @param img2 The second image
* @return Both Manders' M1 and M2 values
*/
public MandersResults calculateMandersCorrelation(Image<T> img1, Image<T> img2) {
// get the cursors for iterating through pixels in images
Cursor<T> cursor1 = img1.createCursor();
Cursor<T> cursor2 = img2.createCursor();
double m1Nominator = 0;
double m2Nominator = 0;
double sumCh1 = 0;
double sumCh2 = 0;
// iterate over images
while (cursor1.hasNext() && cursor2.hasNext()) {
cursor1.fwd();
cursor2.fwd();
T type1 = cursor1.getType();
double ch1 = type1.getRealDouble();
T type2 = cursor2.getType();
double ch2 = type2.getRealDouble();
// if ch2 is non-zero, increase ch1 nominator
if (Math.abs(ch2) > 0.00001) {
m1Nominator += ch1;
}
// if ch1 is non-zero, increase ch2 nominator
if (Math.abs(ch1) > 0.00001) {
m2Nominator += ch2;
}
sumCh1 += ch1;
sumCh2 += ch2;
}
// close the cursors
cursor1.close();
cursor2.close();
MandersResults results = new MandersResults();
// calculate the results
results.m1 = m1Nominator / sumCh1;
results.m2 = m2Nominator / sumCh2;
return results;
}
/**
* Calculates Manders' split M1 and M2 values from image
* values above certain thresholds.
*
* @param cursor The cursor to walk over the images.
* @param ch1Total Channel ones total sum of image values.
* @param ch2Total Channel twos total sum of image values.
* @return Both Manders M1 and M2 coefficients
*/
public MandersResults calculateMandersCorrelation(TwinValueRangeCursor<T> cursor,
double ch1Total, double ch2Total) {
double m1Nominator = 0;
double m2Nominator = 0;
// iterate over images
while ( cursor.hasNext() ) {
cursor.fwd();
T type1 = cursor.getChannel1Type();
double ch1 = type1.getRealDouble();
T type2 = cursor.getChannel2Type();
double ch2 = type2.getRealDouble();
// if ch2 is non-zero, increase ch1 nominator
if (Math.abs(ch2) > 0.00001) {
m1Nominator += ch1;
}
// if ch1 is non-zero, increase ch2 nominator
if (Math.abs(ch1) > 0.00001) {
m2Nominator += ch2;
}
}
// close the cursor
cursor.close();
MandersResults results = new MandersResults();
// calculate the results
results.m1 = m1Nominator / ch1Total;
results.m2 = m2Nominator / ch2Total;
return results;
}
@Override
public void processResults(ResultHandler<T> handler) {
super.processResults(handler);
handler.handleValue( "Manders M1 (no threshold)", mandersM1 );
handler.handleValue( "Manders M2 (no threshold)", mandersM2 );
handler.handleValue( "Manders M1 (threshold)", mandersThresholdedM1 );
handler.handleValue( "Manders M2 (threshold)", mandersThresholdedM2 );
}
}
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