/**
* Get standard deviation of the image
*
* @param img input image
* @return sigma
*/
protected double getStdDev(MTBImage img) {
int sizeStack = img.getSizeStack();
int sizeX = img.getSizeX();
int sizeY = img.getSizeY();
double val;
double mu = 0.0;
double mu2 = 0.0;
double N = 0.0;
double sigma;
for (int i = 0; i < sizeStack; i++) {
img.setCurrentSliceIndex(i);
for (int y = 0; y < sizeY; y++) {
for (int x = 0; x < sizeX; x++) {
val = img.getValueDouble(x, y);
mu += val;
mu2 += val * val;
N++;
}
}
}
mu /= N;
mu2 /= N;
sigma = Math.sqrt(mu2 - mu * mu);
return sigma;
}
/**
* Denoise wavelet coefficients using Jeffrey's noninformative prior for a given sigma of noise
*
* @param img input image
* @param sigma sigma of noise
*/
protected void denoise(MTBImage img, double sigma) {
int sizeStack = img.getSizeStack();
int sizeX = img.getSizeX();
int sizeY = img.getSizeY();
double s2 = 3.0 * sigma * sigma;
double val, val2;
for (int i = 0; i < sizeStack; i++) {
img.setCurrentSliceIndex(i);
for (int y = 0; y < sizeY; y++) {
for (int x = 0; x < sizeX; x++) {
val = img.getValueDouble(x, y);
val2 = (val * val - s2);
if (val2 < 0.0) val2 = 0.0;
if (val != 0.0) img.putValueDouble(x, y, val2 / val);
else img.putValueDouble(x, y, 0.0);
}
}
}
img.setCurrentSliceIndex(0);
}
/**
* @param inImg
* @param trajectories
*/
public TrackVisualizer(MTBImage inImg, Vector<Trajectory2D> trajectories) {
this.inImg = inImg;
this.trajectories = trajectories;
this.sizeX = inImg.getSizeX();
this.sizeY = inImg.getSizeY();
this.sizeT = inImg.getSizeT();
}
