Hello David

Your answer was very helpful to me. The solution you have found is great and helps me a lot. I like the way how you used manipulate[]. Thank you very much, David!!!

Best reads,

Peter

One problem with your approach is that you are setting your threshold above a fixed value corresponding to the one value at the beginning of your data. Your image does not correspond only to that value with a few other values well above it. Therefore you are seeing all values above your artificial threshold. Your friend's code (which I am not sure what it means exactly as it does not reference any image data and it seems to be looking for values above a threshold of 46700 which has nothing to do with your data...) seems to be looking for data above a threshold. And this is the approach that one would take here.

Let's assume that your image is called image. Then take a look at the following set of steps:

Here is a histogram of your data

Histogram[Flatten[ImageData@image]] 

Here are the actual data values:

Sort[Union[Flatten[ImageData@image]]] 

And here are the values along with their frequencies:

{First[#], Length[#]} & /@ Split[Sort[Flatten[ImageData@image]]] 

So the question is where do you want to set your threshold for identifying your "outliers". One can argue from the previous result that the values above 0.7 are of interest to you. So let's identify them (resizing the result so that it is more readable):

ImageResize[Image[ImageData[image] /. {_?(# > .7 &) :> 1}], 600] 

And you can explore the threshold with a Manipulate.

Manipulate[
 ImageResize[Image[ImageData[image] /. {_?(# > threshhold &) :> 1}], 600],
 {threshhold, .5, 1}] 

See the attached notebook...

I hope this helps.

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