Optimal plugins and tools to this type of image

[Adam Hughes]

Hi guys,

I have gray sem images of gold nanoparticles on a glass surface.  Due to
various aspects of the SEM, it tends to be rather difficult for us to get
extremely clear resolution for particle boundaries, and the sizes of the
particles are quite sensitive to manual thresholding.

I know that there are a few builtin tools (like watershedding) to help
resolve the separation of neighboring particles, and there are likely
several plugins that I'm not aware of that could be useful helpful.

My goal is basically as follows:

   Choose the "optimal" threshold that retains small particle-particle
separations but does not undersize the particles.

Higher thresholds encapsulate the full particles but also degrade
specificity in boundaries.

I'm basically amenable to any plugins or tools that could help me get more
accurate particle counts and size estimations, and thought maybe you guys
would know of some plugins/filters or other tools that I'm not aware of.

And on a final note, can you guys think of a way to identify x-sized
regions that are unoccupied?  You can see in the image there are a few big
spaces where not particles occupy the area.  If I had a scale set and
wanted imagej to find any region with no particles in it that exceeds, say,
100 square nanometers, is something like this directly possible?

Thanks.

--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html

[Aryeh Weiss]

Try the following (recorded with the macro recorder):

run("Smooth");
run("Smooth");
run("Find Maxima...", "noise=5 output=[Segmented Particles] exclude");
imageCalculator("AND create", "sem_test.png","sem_test.png Segmented");

I attached the result of the AND and the output of the find maxima command.
Edge maxima are excluded.

Depending on the variation between images , you may need to vary the
noise threshold.

--aryeh



On 3/11/13 11:11 PM, Adam Hughes wrote:

> Hi guys,
>
> I have gray sem images of gold nanoparticles on a glass surface.  Due to
> various aspects of the SEM, it tends to be rather difficult for us to get
> extremely clear resolution for particle boundaries, and the sizes of the
> particles are quite sensitive to manual thresholding.
>
> I know that there are a few builtin tools (like watershedding) to help
> resolve the separation of neighboring particles, and there are likely
> several plugins that I'm not aware of that could be useful helpful.
>
> My goal is basically as follows:
>
>     Choose the "optimal" threshold that retains small particle-particle
> separations but does not undersize the particles.
>
> Higher thresholds encapsulate the full particles but also degrade
> specificity in boundaries.
>
> I'm basically amenable to any plugins or tools that could help me get more
> accurate particle counts and size estimations, and thought maybe you guys
> would know of some plugins/filters or other tools that I'm not aware of.
>
> And on a final note, can you guys think of a way to identify x-sized
> regions that are unoccupied?  You can see in the image there are a few big
> spaces where not particles occupy the area.  If I had a scale set and
> wanted imagej to find any region with no particles in it that exceeds, say,
> 100 square nanometers, is something like this directly possible?
>
> Thanks.
>
> --
> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>

--
Aryeh Weiss
Faculty of Engineering
Bar Ilan University
Ramat Gan 52900 Israel

Ph:  972-3-5317638
FAX: [phone]


--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html

[Adam Hughes]

Thanks!

On Tue, Mar 12, 2013 at 1:14 AM, Aryeh Weiss <[hidden email]> wrote:

> Try the following (recorded with the macro recorder):
>
> run("Smooth");
> run("Smooth");
> run("Find Maxima...", "noise=5 output=[Segmented Particles] exclude");
> imageCalculator("AND create", "sem_test.png","sem_test.png Segmented");
>
> I attached the result of the AND and the output of the find maxima command.
> Edge maxima are excluded.
>
> Depending on the variation between images , you may need to vary the noise
> threshold.
>
> --aryeh
>
>
>
> On 3/11/13 11:11 PM, Adam Hughes wrote:
>
>> Hi guys,
>>
>> I have gray sem images of gold nanoparticles on a glass surface.  Due to
>> various aspects of the SEM, it tends to be rather difficult for us to get
>> extremely clear resolution for particle boundaries, and the sizes of the
>> particles are quite sensitive to manual thresholding.
>>
>> I know that there are a few builtin tools (like watershedding) to help
>> resolve the separation of neighboring particles, and there are likely
>> several plugins that I'm not aware of that could be useful helpful.
>>
>> My goal is basically as follows:
>>
>>     Choose the "optimal" threshold that retains small particle-particle
>> separations but does not undersize the particles.
>>
>> Higher thresholds encapsulate the full particles but also degrade
>> specificity in boundaries.
>>
>> I'm basically amenable to any plugins or tools that could help me get more
>> accurate particle counts and size estimations, and thought maybe you guys
>> would know of some plugins/filters or other tools that I'm not aware of.
>>
>> And on a final note, can you guys think of a way to identify x-sized
>> regions that are unoccupied?  You can see in the image there are a few big
>> spaces where not particles occupy the area.  If I had a scale set and
>> wanted imagej to find any region with no particles in it that exceeds,
>> say,
>> 100 square nanometers, is something like this directly possible?
>>
>> Thanks.
>>
>> --
>> ImageJ mailing list: http://imagej.nih.gov/ij/list.**html<http://imagej.nih.gov/ij/list.html>
>>
>>
>
> --
> Aryeh Weiss
> Faculty of Engineering
> Bar Ilan University
> Ramat Gan 52900 Israel
>
> Ph:  972-3-5317638
> FAX: [phone]
>
>
> --
> ImageJ mailing list: http://imagej.nih.gov/ij/list.**html<http://imagej.nih.gov/ij/list.html>
>

--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html

[Romain Guiet]

Hello,

You will find enclosed a macro to run on your image.

Using “Laplacian of Gaussian” we create a mask and then identify particles. From the coordinates of these particles, the macro draws a mask to connect particles that are closer, to each other, than a user defined threshold (40 pixels). This allows for the identification of empty areas (define a Threshold size, 500 pixel^2).

We just “chose” values for parameters mentioned above; you may have to adjust them.

We hope this would help,

Thanks a lot for this study case :D

Romain and Olivier



/////////////////////////////////////////////////////////////////////////////////
//macro_start

//clear Results, ROIs, set Foreground White
run("Clear Results");
roiManager("Reset");
setForegroundColor(255, 255, 255);

//GetImageTitle, create a newImage with same dimensions
title=getTitle();
getDimensions(width, height, channels, slices, frames);
run("Duplicate...", "title=16-bit");
newImage("Draw", "8-bit Black", width, height, 1);


selectWindow(title);
//Image is RGB, convert to 16-bit
run("16-bit");


//Extracting blob-like Features using "Lapalcian of Gaussian"
run("FeatureJ Laplacian", "compute smoothing=2");
rename("Mask");

//Threshold, Watershed and Erode (improve blob separation)
setAutoThreshold("Default");
run("Convert to Mask");
run("Watershed");
run("Options...", "iterations=2 count=4 black edm=Overwrite do=Erode");

//Analyze particles
run("Analyze Particles...", "size=0-Infinity circularity=0.00-1.00 show=Nothing display exclude summarize add");
roiManager("Show All without labels");
roiManager("Show All");


//////////////////////////////////////////////
// Identify empty areas

// Adjust these values to your needs
//Set "distance threshold" value
//and Empty Area minimal size
distanceThreshold = 40;
emptyMinimalArea=500;


//get center of Mass of each particles
roiNumber = roiManager("count");
xCoordinates = newArray(roiNumber);
yCoordinates = newArray(roiNumber);
for (i=0; i< roiNumber ; i++){
        xCoordinates[i]= getResult("XM",i) ;
        yCoordinates[i]= getResult("YM",i) ;
}

//Measure Distance, if under a distance Threshold => draw a line.
setBatchMode(true);
for (ii=0; ii< roiNumber ; ii++){
        xRef = xCoordinates[ii];
        yRef = yCoordinates[ii];
        distance = newArray(roiNumber);

        for (iii=ii+1; iii< roiNumber ; iii++){
                distance[iii]=sqrt(pow((xRef-xCoordinates[iii] ),2) + pow((yRef-yCoordinates[iii]),2));
                if (distance[iii] < distanceThreshold){
                        selectWindow("Draw");
                        drawLine(xRef, yRef, xCoordinates[iii], yCoordinates[iii]);
                }
        }
}
setBatchMode(false);
selectWindow("Draw");
run("Duplicate...", "title=Empty Areas");

//threshold and binary Open to "thicken" drawn lines. (help analyze particles)
setThreshold(0, 128);
run("Convert to Mask");
run("Options...", "iterations=1 count=1 black pad edm=Overwrite do=Erode");

//Analyze particles find empty areas

run("Analyze Particles...", "size="+emptyMinimalArea+"-Infinity circularity=0.00-1.00 show=Nothing display exclude summarize add");

selectWindow(title);
roiManager("Show All without labels");
roiManager("Show All");


//macro_end
///////////////////////////////////////////////////////////////////////////////////////////

---------------------------------------------------------------
Dr. Romain Guiet
Bioimaging and Optics Platform (PT-BIOP)
Ecole Polytechnique Fédérale de Lausanne (EPFL)
Faculty of Life Sciences
Station 19, AI 0241
CH-1015 Lausanne

Phone: [+4121 69] 39629
http://biop.epfl.ch/
---------------------------------------------------------------

________________________________________
De : ImageJ Interest Group [[hidden email]] de la part de Aryeh Weiss [[hidden email]]
Date d'envoi : mardi 12 mars 2013 06:14
À : [hidden email]
Objet : Re: Optimal plugins and tools to this type of image

Try the following (recorded with the macro recorder):

run("Smooth");
run("Smooth");
run("Find Maxima...", "noise=5 output=[Segmented Particles] exclude");
imageCalculator("AND create", "sem_test.png","sem_test.png Segmented");

I attached the result of the AND and the output of the find maxima command.
Edge maxima are excluded.

Depending on the variation between images , you may need to vary the
noise threshold.

--aryeh



On 3/11/13 11:11 PM, Adam Hughes wrote:

> Hi guys,
>
> I have gray sem images of gold nanoparticles on a glass surface.  Due to
> various aspects of the SEM, it tends to be rather difficult for us to get
> extremely clear resolution for particle boundaries, and the sizes of the
> particles are quite sensitive to manual thresholding.
>
> I know that there are a few builtin tools (like watershedding) to help
> resolve the separation of neighboring particles, and there are likely
> several plugins that I'm not aware of that could be useful helpful.
>
> My goal is basically as follows:
>
>     Choose the "optimal" threshold that retains small particle-particle
> separations but does not undersize the particles.
>
> Higher thresholds encapsulate the full particles but also degrade
> specificity in boundaries.
>
> I'm basically amenable to any plugins or tools that could help me get more
> accurate particle counts and size estimations, and thought maybe you guys
> would know of some plugins/filters or other tools that I'm not aware of.
>
> And on a final note, can you guys think of a way to identify x-sized
> regions that are unoccupied?  You can see in the image there are a few big
> spaces where not particles occupy the area.  If I had a scale set and
> wanted imagej to find any region with no particles in it that exceeds, say,
> 100 square nanometers, is something like this directly possible?
>
> Thanks.
>
> --
> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>

--
Aryeh Weiss
Faculty of Engineering
Bar Ilan University
Ramat Gan 52900 Israel

Ph:  972-3-5317638
FAX: [phone]


--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html

--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html

[Romain Guiet]

Hello,

You will find below a macro to run on your image ( I encounted ome trouble with the previous mail...)

Using “Laplacian of Gaussian” we create a mask and then identify particles. From the coordinates of these particles, the macro draws a mask to connect particles that are closer, to each other, than a user defined threshold (40 pixels). This allows for the identification of empty areas (define a Threshold size, 500 pixel^2).

We just “chose” values for parameters mentioned above; you may have to adjust them.

We hope this would help,

Thanks a lot for this study case :D

Romain and Olivier



/////////////////////////////////////////////////////////////////////////////////
//macro_start

//clear Results, ROIs, set Foreground White
run("Clear Results");
roiManager("Reset");
setForegroundColor(255, 255, 255);

//GetImageTitle, create a newImage with same dimensions
title=getTitle();
getDimensions(width, height, channels, slices, frames);
run("Duplicate...", "title=16-bit");
newImage("Draw", "8-bit Black", width, height, 1);


selectWindow(title);
//Image is RGB, convert to 16-bit
run("16-bit");


//Extracting blob-like Features using "Lapalcian of Gaussian"
run("FeatureJ Laplacian", "compute smoothing=2");
rename("Mask");

//Threshold, Watershed and Erode (improve blob separation)
setAutoThreshold("Default");
run("Convert to Mask");
run("Watershed");
run("Options...", "iterations=2 count=4 black edm=Overwrite do=Erode");

//Analyze particles
run("Analyze Particles...", "size=0-Infinity circularity=0.00-1.00 show=Nothing display exclude summarize add");
roiManager("Show All without labels");
roiManager("Show All");


//////////////////////////////////////////////
// Identify empty areas

// Adjust these values to your needs
//Set "distance threshold" value
//and Empty Area minimal size
distanceThreshold = 40;
emptyMinimalArea=500;


//get center of Mass of each particles
roiNumber = roiManager("count");
xCoordinates = newArray(roiNumber);
yCoordinates = newArray(roiNumber);
for (i=0; i< roiNumber ; i++){
        xCoordinates[i]= getResult("XM",i) ;
        yCoordinates[i]= getResult("YM",i) ;
}

//Measure Distance, if under a distance Threshold => draw a line.
setBatchMode(true);
for (ii=0; ii< roiNumber ; ii++){
        xRef = xCoordinates[ii];
        yRef = yCoordinates[ii];
        distance = newArray(roiNumber);

        for (iii=ii+1; iii< roiNumber ; iii++){
                distance[iii]=sqrt(pow((xRef-xCoordinates[iii] ),2) + pow((yRef-yCoordinates[iii]),2));
                if (distance[iii] < distanceThreshold){
                        selectWindow("Draw");
                        drawLine(xRef, yRef, xCoordinates[iii], yCoordinates[iii]);
                }
        }
}
setBatchMode(false);
selectWindow("Draw");
run("Duplicate...", "title=Empty Areas");

//threshold and binary Open to "thicken" drawn lines. (help analyze particles)
setThreshold(0, 128);
run("Convert to Mask");
run("Options...", "iterations=1 count=1 black pad edm=Overwrite do=Erode");

//Analyze particles find empty areas

run("Analyze Particles...", "size="+emptyMinimalArea+"-Infinity circularity=0.00-1.00 show=Nothing display exclude summarize add");

selectWindow(title);
roiManager("Show All without labels");
roiManager("Show All");


//macro_end
///////////////////////////////////////////////////////////////////////////////////////////



---------------------------------------------------------------
Dr. Romain Guiet
Bioimaging and Optics Platform (PT-BIOP)
Ecole Polytechnique Fédérale de Lausanne (EPFL)
Faculty of Life Sciences
Station 19, AI 0241
CH-1015 Lausanne

Phone: [+4121 69] 39629
http://biop.epfl.ch/
---------------------------------------------------------------

________________________________________
De : ImageJ Interest Group [[hidden email]] de la part de Aryeh Weiss [[hidden email]]
Date d'envoi : mardi 12 mars 2013 06:14
À : [hidden email]
Objet : Re: Optimal plugins and tools to this type of image

Try the following (recorded with the macro recorder):

run("Smooth");
run("Smooth");
run("Find Maxima...", "noise=5 output=[Segmented Particles] exclude");
imageCalculator("AND create", "sem_test.png","sem_test.png Segmented");

I attached the result of the AND and the output of the find maxima command.
Edge maxima are excluded.

Depending on the variation between images , you may need to vary the
noise threshold.

--aryeh



On 3/11/13 11:11 PM, Adam Hughes wrote:

> Hi guys,
>
> I have gray sem images of gold nanoparticles on a glass surface.  Due to
> various aspects of the SEM, it tends to be rather difficult for us to get
> extremely clear resolution for particle boundaries, and the sizes of the
> particles are quite sensitive to manual thresholding.
>
> I know that there are a few builtin tools (like watershedding) to help
> resolve the separation of neighboring particles, and there are likely
> several plugins that I'm not aware of that could be useful helpful.
>
> My goal is basically as follows:
>
>     Choose the "optimal" threshold that retains small particle-particle
> separations but does not undersize the particles.
>
> Higher thresholds encapsulate the full particles but also degrade
> specificity in boundaries.
>
> I'm basically amenable to any plugins or tools that could help me get more
> accurate particle counts and size estimations, and thought maybe you guys
> would know of some plugins/filters or other tools that I'm not aware of.
>
> And on a final note, can you guys think of a way to identify x-sized
> regions that are unoccupied?  You can see in the image there are a few big
> spaces where not particles occupy the area.  If I had a scale set and
> wanted imagej to find any region with no particles in it that exceeds, say,
> 100 square nanometers, is something like this directly possible?
>
> Thanks.
>
> --
> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>

--
Aryeh Weiss
Faculty of Engineering
Bar Ilan University
Ramat Gan 52900 Israel

Ph:  972-3-5317638
FAX: [phone]


--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html

--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html

[gankaku]

Hi Adam,

The macro from Romain and Olivier (which was a great idea) inspired me to try something similar. Here's the output.
Just some specifications:
1.) it enables you to directly input the minimal size of the empty spaces you want to measure
2.) it pre-processes the image slightly (see macro code)
3.) it uses a Difference of Gaussian instead of Laplacian and lets you specify the Gaussian blurring steps (1 and 3 are default and worked fine for your sample image) --> in my opinion thus the objects/particles are thresholded a little better (but that's subjective perception).
4.) most importantly it skips the drawing step and thus defines the empty spaces only in areas which do not overlap with the objects
5.) the output shows you the interspaces characterized as overlay over the original image, you have those still in the ROI manager and get result tables for all objects as well as the characterized interspaces.

Additionally you could also get an outline map of the objects with numbers by changing the "Nothing" statement in the run("Analyze..." code line to Outlines.

If something does not work as expected, just send me an e-mail. And I'm sorry for the little bit chaotic code.

Hope it helps you and others!
Best regards,
Jan (www.biovoxxel.de)

And here's the code:


//macro start------------------------------------------------------------------------------------------------------------------
//option settings
setBatchMode(true);
run("Options...", "iterations=1 count=1 black edm=Overwrite");
run("Set Measurements...", "area mean standard modal min centroid center perimeter bounding fit shape feret's integrated median skewness kurtosis area_fraction stack redirect=None decimal=3");
title=getTitle();

//Dialog to define minimal interspace size
Dialog.create("Define minimal interspace size");
        Dialog.addNumber("minimal interspace size (pixel^2)", 1000);
        Dialog.show();
        minSize = Dialog.getNumber();

//Pre-processing of original image
run("16-bit");
run("Median...", "radius=2");
run("Enhance Contrast...", "saturated=0 normalize");

//Calling the "Difference of Gaussian" function
DifferenceOfGaussian();

//thresholding and analyzing objects
selectWindow("DoG of "+title);
objects=getTitle();
setAutoThreshold("Otsu dark");
run("Convert to Mask");
run("Watershed");
run("Analyze Particles...", "size=0-Infinity circularity=0.00-1.00 show=Nothing display clear");
IJ.renameResults("Objects");

//thresholding and analyzing of interspaces
selectWindow(objects);
run("Duplicate...", "title=Interspaces");
run("Invert");
run("Watershed");
run("Analyze Particles...", "size="+minSize+"-Infinity circularity=0.00-1.00 show=Nothing display clear add");
IJ.renameResults("Interspaces");

//Indicate interspaces over original image
selectWindow(title);
run("Revert");
roiManager("Show All with labels");
roiManager("Show All");


function DifferenceOfGaussian() {

title=getTitle();
newtitle="DoG of " + title;
Dialog.create("Gaussian Blur");
        Dialog.addMessage("Choose Gaussian Blur settings");
        Dialog.addNumber("smaller sigma:", 1);
        Dialog.addNumber("greater sigma (3-6x higher):", 3);
        sig1 = Dialog.getNumber();
        sig2 = Dialog.getNumber();
        //factor = Dialog.getChoice();
        Dialog.show();
setBatchMode(true);
run("Duplicate...", "title=DoG");
rename(newtitle);
run("Duplicate...", "title=intermediate");
selectWindow("intermediate");
run("Gaussian Blur...", "sigma=sig2");
run("Select All");
run("Copy");
selectWindow(newtitle);
run("Gaussian Blur...", "sigma=sig1");
setPasteMode("Subtract");
setBatchMode(false);
run("Paste");

}

//end------------------------------------------------------------------------------------------------------------------

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[Adam Hughes]

These suggestions are all very impressive guys.  I will try some of them
out here asap.

Thanks very much.

On Tue, Mar 12, 2013 at 12:29 PM, Jan Brocher - BioVoxxel <
[hidden email]> wrote:

> Hi Adam,
>
> The macro from Romain and Olivier (which was a great idea) inspired me to
> try something similar. Here's the output.
> Just some specifications:
> 1.) it enables you to directly input the minimal size of the empty spaces
> you want to measure
> 2.) it pre-processes the image slightly (see macro code)
> 3.) it uses a Difference of Gaussian instead of Laplacian and lets you
> specify the Gaussian blurring steps (1 and 3 are default and worked fine
> for your sample image) --> in my opinion thus the objects/particles are
> thresholded a little better (but that's subjective perception).
> 4.) most importantly it skips the drawing step and thus defines the empty
> spaces only in areas which do not overlap with the objects
> 5.) the output shows you the interspaces characterized as overlay over the
> original image, you have those still in the ROI manager and get result
> tables for all objects as well as the characterized interspaces.
>
> Additionally you could also get an outline map of the objects with numbers
> by changing the "Nothing" statement in the run("Analyze..." code line to
> Outlines.
>
> If something does not work as expected, just send me an e-mail. And I'm
> sorry for the little bit chaotic code.
>
> Hope it helps you and others!
> Best regards,
> Jan (www.biovoxxel.de)
>
> And here's the code:
>
>
> //macro
> start------------------------------------------------------------------------------------------------------------------
> //option settings
> setBatchMode(true);
> run("Options...", "iterations=1 count=1 black edm=Overwrite");
> run("Set Measurements...", "area mean standard modal min centroid center
> perimeter bounding fit shape feret's integrated median skewness kurtosis
> area_fraction stack redirect=None decimal=3");
> title=getTitle();
>
> //Dialog to define minimal interspace size
> Dialog.create("Define minimal interspace size");
>         Dialog.addNumber("minimal interspace size (pixel^2)", 1000);
>         Dialog.show();
>         minSize = Dialog.getNumber();
>
> //Pre-processing of original image
> run("16-bit");
> run("Median...", "radius=2");
> run("Enhance Contrast...", "saturated=0 normalize");
>
> //Calling the "Difference of Gaussian" function
> DifferenceOfGaussian();
>
> //thresholding and analyzing objects
> selectWindow("DoG of "+title);
> objects=getTitle();
> setAutoThreshold("Otsu dark");
> run("Convert to Mask");
> run("Watershed");
> run("Analyze Particles...", "size=0-Infinity circularity=0.00-1.00
> show=Nothing display clear");
> IJ.renameResults("Objects");
>
> //thresholding and analyzing of interspaces
> selectWindow(objects);
> run("Duplicate...", "title=Interspaces");
> run("Invert");
> run("Watershed");
> run("Analyze Particles...", "size="+minSize+"-Infinity
> circularity=0.00-1.00 show=Nothing display clear add");
> IJ.renameResults("Interspaces");
>
> //Indicate interspaces over original image
> selectWindow(title);
> run("Revert");
> roiManager("Show All with labels");
> roiManager("Show All");
>
>
> function DifferenceOfGaussian() {
>
> title=getTitle();
> newtitle="DoG of " + title;
> Dialog.create("Gaussian Blur");
>         Dialog.addMessage("Choose Gaussian Blur settings");
>         Dialog.addNumber("smaller sigma:", 1);
>         Dialog.addNumber("greater sigma (3-6x higher):", 3);
>         sig1 = Dialog.getNumber();
>         sig2 = Dialog.getNumber();
>         //factor = Dialog.getChoice();
>         Dialog.show();
> setBatchMode(true);
> run("Duplicate...", "title=DoG");
> rename(newtitle);
> run("Duplicate...", "title=intermediate");
> selectWindow("intermediate");
> run("Gaussian Blur...", "sigma=sig2");
> run("Select All");
> run("Copy");
> selectWindow(newtitle);
> run("Gaussian Blur...", "sigma=sig1");
> setPasteMode("Subtract");
> setBatchMode(false);
> run("Paste");
>
> }
>
>
> //end------------------------------------------------------------------------------------------------------------------
>
>
>

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