Phriction Projects Wikis Bioimaging And Optics Platform Teaching II-b FWHM - Measure Lengths Scripts History Version 3 vs 5
Version 3 vs 5
Version 3 vs 5
Edits
Edits
- Edit by oburri, Version 5
- Dec 3 2018 10:45
- Edit by romainGuiet, Version 3
- Jun 5 2018 17:36
| Edit Older Version 3... | Edit Current Version 5... |
Content Changes
Content Changes
== Manual Measure ==
``` lang = javascript
//open an image
run("Confocal Series (2.2MB)");
image_Name = getTitle();
//get some informations about the image
getDimensions(image_width, image_height, image_channels, image_slices, image_frames);
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
// here we'll make a line in the middle of the image
y_line = image_height/2;
makeLine(0, y_line ,image_width , y_line);
// then we'll create a new iamge by re-slicing
// re-sampling through this line in the z-direction
run("Reslice [/]...", "output="+voxel_depth+" slice_count=1");
selectWindow("Reslice of "+image_Name);
// Now you could draw a line and get the length of this line
// ther is a live measure in the main ImageJ/Fiji bar when you draw it
// or you can measure
setTool("line");
while (roiManager("Count") < 1 ){
waitForUser("Please make a line");
if (selectionType > -1 ){
roiManager("Add");
roiManager("Measure");
}
}
```
**BUT** this manual way even if it’s quick can lead to some bias in your measurements, because **You** have to decide **WHERE** you place the begin and the end of the line ?
When you want to measure the diameter of point object like a bead, what is commonly accepted is to measure the Full width at half maximum (have look at FWHM on wikipedia) using a Gaussian fit, but you may need to use a Super Gaussian fit , see below.
``` lang = javascript
// do some cleaning of the environment before starting
run("Close All");
run("Clear Results");
// open an image
// we need a sampling that is larger than the object
// in the previous image stop there were not enough z
// above and below the object
run("T1 Head (2.4M, 16-bits)");
image_Name = getTitle();
//get some informations about the image
getDimensions(image_width, image_height, image_channels, image_slices, image_frames);
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
// here we make a line
makeLine(0, 90, 256, 90);
// then we create a new image by re-slicing
run("Reslice [/]...", "output="+voxel_depth+" slice_count=1");
selectImage(nImages);
resliceImage = getTitle();
//Let's make a line
makeLine(25, 67, 240, 67);
waitForUser("We'll make a profile through this line and 'fit' a gaussian formula on it");
fitGaussian();
waitForUser("The inhomogenity of the signal causes trouble ");
// Let's blur a bit the image
selectImage(resliceImage);
run("Duplicate...", "title=["+resliceImage+"-Blurred]");
run("Gaussian Blur...", "sigma=5");
waitForUser("So we blur the image a bit before doing the fitting");
makeLine(25, 67, 240, 67);
fitGaussian();
selectImage(resliceImage+"-Blurred");
waitForUser("But this is still not 'perfect' \nbecause the length of the line you used \nwill affect your fitting and the results");
makeLine(0, 67, image_width, 67);
fitGaussian();
selectImage(resliceImage+"-Blurred");
waitForUser("One possible way is to fit a 'SuperGaussian', \nbut it needs estimates(see function below)");
makeLine(0, 67, image_width, 67);
fitSUPERGaussian();
// companion function to make the math for FWHM
function fitGaussian(){
y = getProfile();
x = Array.getSequence(lengthOf(y));
Fit.doFit("Gaussian", x, y) ;
Fit.plot;
sortedParameter = Fit.p(3); // parameter d of gaussian
rSquared = Fit.rSquared ;
FWHM = (2 * sqrt( 2 * log(2) ) ) * sortedParameter ;// http://fr.wikipedia.org/wiki/Largeur_%C3%A0_mi-hauteur
setResult("FWHM ("+voxel_unit+")", nResults, FWHM * voxel_height);
setResult("Label",nResults-1,image_Name);
setResult("rSquared",nResults-1,rSquared);
updateResults();
selectWindow("Results");
}
function fitSUPERGaussian(){
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
yCurrent = getProfile();
xPlot = Array.getSequence(lengthOf(yCurrent));
pidiv2 = PI/2;
formulaString = "y = a + b * exp( "+pidiv2+"*(-pow ( abs(x-c), e/2 ) ) / ( pow( d* "+pidiv2+" , e/2 ) ) )";
pourcentOfMax = 50 ; // percent of the Maxmimal value of the Gaussian you want to consider as the diameter, 50 = FWHM
fractionOfMax = 1/pourcentOfMax *100;
baseline=0;
maxOfCurve = 255;
centerOfCurve = 125 ;
fullWithFactor = 200; // estimated diameter
gamma = 2 // is close to 2 for a gaussian
initialGuesses = newArray(baseline, maxOfCurve, centerOfCurve, fullWithFactor, gamma);
Fit.doFit(formulaString, xPlot, yCurrent,initialGuesses);
Fit.plot();
parameterA = Fit.p(0); // baseLine
parameterB = Fit.p(1); // top - baseLine
parameterC = Fit.p(2); // center
parameterD = Fit.p(3); // function of FW
parameterE = Fit.p(4); // N, the great lord gamma
rSquared = Fit.rSquared;
radius = parameterD * pow( (2/PI), (2/parameterE) - 1 ) * pow ( log(fractionOfMax), 2/parameterE) ; // in pixel
setResult("FWHM ("+voxel_unit+")", nResults, 2*radius*voxel_width);
setResult("Label",nResults-1,image_Name);
setResult("rSquared",nResults-1,rSquared);
updateResults();
selectWindow("Results");
}
```
Measuring the Full Width At Half Maximum is a common way to check for the resolution of an image on small structures. Or to measure the length of a structure.
the following scripts help automate this measurement by having the user trace a line profile and ImagEJ taking care of fitting a gaussian function which allows it to compute a FWHM value.
== Manual Measure ==
``` lang = javascript
//open an image
run("Confocal Series (2.2MB)");
image_Name = getTitle();
//get some informations about the image
getDimensions(image_width, image_height, image_channels, image_slices, image_frames);
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
// here we'll make a line in the middle of the image
y_line = image_height/2;
makeLine(0, y_line ,image_width , y_line);
// then we'll create a new iamge by re-slicing
// re-sampling through this line in the z-direction
run("Reslice [/]...", "output="+voxel_depth+" slice_count=1");
selectWindow("Reslice of "+image_Name);
// Now you could draw a line and get the length of this line
// ther is a live measure in the main ImageJ/Fiji bar when you draw it
// or you can measure
setTool("line");
while (roiManager("Count") < 1 ){
waitForUser("Please make a line");
if (selectionType > -1 ){
roiManager("Add");
roiManager("Measure");
}
}
```
**BUT** this manual way even if it’s quick can lead to some bias in your measurements, because **You** have to decide **WHERE** you place the begin and the end of the line ?
When you want to measure the diameter of point object like a bead, what is commonly accepted is to measure the Full width at half maximum (have look at FWHM on wikipedia) using a Gaussian fit, but you may need to use a Super Gaussian fit , see below.
``` lang = javascript
// do some cleaning of the environment before starting
run("Close All");
run("Clear Results");
// open an image
// we need a sampling that is larger than the object
// in the previous image stop there were not enough z
// above and below the object
run("T1 Head (2.4M, 16-bits)");
image_Name = getTitle();
//get some informations about the image
getDimensions(image_width, image_height, image_channels, image_slices, image_frames);
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
// here we make a line
makeLine(0, 90, 256, 90);
// then we create a new image by re-slicing
run("Reslice [/]...", "output="+voxel_depth+" slice_count=1");
selectImage(nImages);
resliceImage = getTitle();
//Let's make a line
makeLine(25, 67, 240, 67);
waitForUser("We'll make a profile through this line and 'fit' a gaussian formula on it");
fitGaussian();
waitForUser("The inhomogenity of the signal causes trouble ");
// Let's blur a bit the image
selectImage(resliceImage);
run("Duplicate...", "title=["+resliceImage+"-Blurred]");
run("Gaussian Blur...", "sigma=5");
waitForUser("So we blur the image a bit before doing the fitting");
makeLine(25, 67, 240, 67);
fitGaussian();
selectImage(resliceImage+"-Blurred");
waitForUser("But this is still not 'perfect' \nbecause the length of the line you used \nwill affect your fitting and the results");
makeLine(0, 67, image_width, 67);
fitGaussian();
selectImage(resliceImage+"-Blurred");
waitForUser("One possible way is to fit a 'SuperGaussian', \nbut it needs estimates(see function below)");
makeLine(0, 67, image_width, 67);
fitSUPERGaussian();
// companion function to make the math for FWHM
function fitGaussian(){
y = getProfile();
x = Array.getSequence(lengthOf(y));
Fit.doFit("Gaussian", x, y) ;
Fit.plot;
sortedParameter = Fit.p(3); // parameter d of gaussian
rSquared = Fit.rSquared ;
FWHM = (2 * sqrt( 2 * log(2) ) ) * sortedParameter ;// http://fr.wikipedia.org/wiki/Largeur_%C3%A0_mi-hauteur
setResult("FWHM ("+voxel_unit+")", nResults, FWHM * voxel_height);
setResult("Label",nResults-1,image_Name);
setResult("rSquared",nResults-1,rSquared);
updateResults();
selectWindow("Results");
}
function fitSUPERGaussian(){
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
yCurrent = getProfile();
xPlot = Array.getSequence(lengthOf(yCurrent));
pidiv2 = PI/2;
formulaString = "y = a + b * exp( "+pidiv2+"*(-pow ( abs(x-c), e/2 ) ) / ( pow( d* "+pidiv2+" , e/2 ) ) )";
pourcentOfMax = 50 ; // percent of the Maxmimal value of the Gaussian you want to consider as the diameter, 50 = FWHM
fractionOfMax = 1/pourcentOfMax *100;
baseline=0;
maxOfCurve = 255;
centerOfCurve = 125 ;
fullWithFactor = 200; // estimated diameter
gamma = 2 // is close to 2 for a gaussian
initialGuesses = newArray(baseline, maxOfCurve, centerOfCurve, fullWithFactor, gamma);
Fit.doFit(formulaString, xPlot, yCurrent,initialGuesses);
Fit.plot();
parameterA = Fit.p(0); // baseLine
parameterB = Fit.p(1); // top - baseLine
parameterC = Fit.p(2); // center
parameterD = Fit.p(3); // function of FW
parameterE = Fit.p(4); // N, the great lord gamma
rSquared = Fit.rSquared;
radius = parameterD * pow( (2/PI), (2/parameterE) - 1 ) * pow ( log(fractionOfMax), 2/parameterE) ; // in pixel
setResult("FWHM ("+voxel_unit+")", nResults, 2*radius*voxel_width);
setResult("Label",nResults-1,image_Name);
setResult("rSquared",nResults-1,rSquared);
updateResults();
selectWindow("Results");
}
```
Measuring the Full Width At Half Maximum is a common way to check for the resolution of an image on small structures. Or to measure the length of a structure.
the following scripts help automate this measurement by having the user trace a line profile and ImagEJ taking care of fitting a gaussian function which allows it to compute a FWHM value.
== Manual Measure ==
``` lang = javascript
//open an image
run("Confocal Series (2.2MB)");
image_Name = getTitle();
//get some informations about the image
getDimensions(image_width, image_height, image_channels, image_slices, image_frames);
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
// here we'll make a line in the middle of the image
y_line = image_height/2;
makeLine(0, y_line ,image_width , y_line);
// then we'll create a new iamge by re-slicing
// re-sampling through this line in the z-direction
run("Reslice [/]...", "output="+voxel_depth+" slice_count=1");
selectWindow("Reslice of "+image_Name);
// Now you could draw a line and get the length of this line
// ther is a live measure in the main ImageJ/Fiji bar when you draw it
// or you can measure
setTool("line");
while (roiManager("Count") < 1 ){
waitForUser("Please make a line");
if (selectionType > -1 ){
roiManager("Add");
roiManager("Measure");
}
}
```
**BUT** this manual way even if it’s quick can lead to some bias in your measurements, because **You** have to decide **WHERE** you place the begin and the end of the line ?
When you want to measure the diameter of point object like a bead, what is commonly accepted is to measure the Full width at half maximum (have look at FWHM on wikipedia) using a Gaussian fit, but you may need to use a Super Gaussian fit , see below.
``` lang = javascript
// do some cleaning of the environment before starting
run("Close All");
run("Clear Results");
// open an image
// we need a sampling that is larger than the object
// in the previous image stop there were not enough z
// above and below the object
run("T1 Head (2.4M, 16-bits)");
image_Name = getTitle();
//get some informations about the image
getDimensions(image_width, image_height, image_channels, image_slices, image_frames);
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
// here we make a line
makeLine(0, 90, 256, 90);
// then we create a new image by re-slicing
run("Reslice [/]...", "output="+voxel_depth+" slice_count=1");
selectImage(nImages);
resliceImage = getTitle();
//Let's make a line
makeLine(25, 67, 240, 67);
waitForUser("We'll make a profile through this line and 'fit' a gaussian formula on it");
fitGaussian();
waitForUser("The inhomogenity of the signal causes trouble ");
// Let's blur a bit the image
selectImage(resliceImage);
run("Duplicate...", "title=["+resliceImage+"-Blurred]");
run("Gaussian Blur...", "sigma=5");
waitForUser("So we blur the image a bit before doing the fitting");
makeLine(25, 67, 240, 67);
fitGaussian();
selectImage(resliceImage+"-Blurred");
waitForUser("But this is still not 'perfect' \nbecause the length of the line you used \nwill affect your fitting and the results");
makeLine(0, 67, image_width, 67);
fitGaussian();
selectImage(resliceImage+"-Blurred");
waitForUser("One possible way is to fit a 'SuperGaussian', \nbut it needs estimates(see function below)");
makeLine(0, 67, image_width, 67);
fitSUPERGaussian();
// companion function to make the math for FWHM
function fitGaussian(){
y = getProfile();
x = Array.getSequence(lengthOf(y));
Fit.doFit("Gaussian", x, y) ;
Fit.plot;
sortedParameter = Fit.p(3); // parameter d of gaussian
rSquared = Fit.rSquared ;
FWHM = (2 * sqrt( 2 * log(2) ) ) * sortedParameter ;// http://fr.wikipedia.org/wiki/Largeur_%C3%A0_mi-hauteur
setResult("FWHM ("+voxel_unit+")", nResults, FWHM * voxel_height);
setResult("Label",nResults-1,image_Name);
setResult("rSquared",nResults-1,rSquared);
updateResults();
selectWindow("Results");
}
function fitSUPERGaussian(){
getVoxelSize(voxel_width, voxel_height, voxel_depth, voxel_unit);
yCurrent = getProfile();
xPlot = Array.getSequence(lengthOf(yCurrent));
pidiv2 = PI/2;
formulaString = "y = a + b * exp( "+pidiv2+"*(-pow ( abs(x-c), e/2 ) ) / ( pow( d* "+pidiv2+" , e/2 ) ) )";
pourcentOfMax = 50 ; // percent of the Maxmimal value of the Gaussian you want to consider as the diameter, 50 = FWHM
fractionOfMax = 1/pourcentOfMax *100;
baseline=0;
maxOfCurve = 255;
centerOfCurve = 125 ;
fullWithFactor = 200; // estimated diameter
gamma = 2 // is close to 2 for a gaussian
initialGuesses = newArray(baseline, maxOfCurve, centerOfCurve, fullWithFactor, gamma);
Fit.doFit(formulaString, xPlot, yCurrent,initialGuesses);
Fit.plot();
parameterA = Fit.p(0); // baseLine
parameterB = Fit.p(1); // top - baseLine
parameterC = Fit.p(2); // center
parameterD = Fit.p(3); // function of FW
parameterE = Fit.p(4); // N, the great lord gamma
rSquared = Fit.rSquared;
radius = parameterD * pow( (2/PI), (2/parameterE) - 1 ) * pow ( log(fractionOfMax), 2/parameterE) ; // in pixel
setResult("FWHM ("+voxel_unit+")", nResults, 2*radius*voxel_width);
setResult("Label",nResults-1,image_Name);
setResult("rSquared",nResults-1,rSquared);
updateResults();
selectWindow("Results");
}
```
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