Dividing Ground-penetrating radar profile through image processing - matlab

Question

Please look at the attached image. It is a GPR profile and using image processing techniques, I am trying to divide this image into 3 zones by labeling with colors the whole image on the top:
when parabolas in the image are very clear and distinct with high pixel values - green zone/ line at the top
when parabolas in the image are blurred but visible - yellow zone
when parabolas are distorted or when not parabolas are present - red zone
What techniques should I use? What's the best approach to solve it?
I have tried various techniques but not with success in every case, because, as you can in the following image, sometimes the parabolas are too close to one another and identifying them is becoming an issue.
A sample of how I want to zone it: https://www.dropbox.com/s/9zm9epgf0gt7591/sample.png?dl=0
One of the tried code: simplest one.
clear all
clc
%read png image
H=imread ('origpng.png');
%convert to gray scale
I = rgb2gray(H);
I(I>150)=0;I(I<100)=0;
figure,imshow(I)
J=I;
J=255-J;
figure, imshow (J)
J(J<255)=0;
figure,imshow (J)

Answer 1

Your question is not very clearly posed, but I spent some time on it and felt like sharing my thoughts. I am not preteding for an instant that this is anywhere near a complete, or rigorous answer - just some musings that might give you some ideas. Also, I use ImageMagick, but if you have and know Matlab, you should use that - I am not suggesting you switch tools.
First, I did a Canny Edge detection like this:
convert http://i.stack.imgur.com/XITAE.png -canny 0x1+15%+50% canny.jpg
that gives me this:
I then "squash" that down till it is just 1 pixel high, which effectively totals up and averages all the columns - I make it 10 pixels high here so you can see it. Where it is white, there are lots of parabolas, elsewhere there are fewer.
Then I stretch that back up to the full height of the original image and blur it a bit - note that everything up to the following image is just one line of "code":
convert http://i.stack.imgur.com/XITAE.png -canny 0x1+15%+50% -resize x1! -normalize -resize 827x310! -blur 0x11 -colorspace gray mask.png
I then use the above as an opacity mask for a red image the same size as your original like this:
convert -size 827x310! xc:red mask.png -compose copy-opacity -composite colouredmask.png
Then I took your original image and coloured it with yellow like this by first creating a yellow image and then blending it onto your image and then I blended the red image from above on top of that:
convert -size 827x310! xc:yellow yellow.png
convert http://i.stack.imgur.com/XITAE.png yellow.png -compose colorize -composite colouredmask.png -compose overlay -composite result.png
giving
Obviously you can set different parameters and use different thresholds and things, but it kind of heads towards the sort of thing you are aiming it.
So the entire process is:
# Make mask of peaky areas - line 1
convert http://i.stack.imgur.com/XITAE.png -canny 0x1+15%+50% -resize x1! -normalize -resize 827x310! -blur 0x11 -colorspace gray mask.png
# Colour mask with red - line 2
convert -size 827x310! xc:red mask.png -compose copy-opacity -composite colouredmask.png
# Tint original image with yellow and then overlay semi-transparent red area
convert -size 827x310! xc:yellow yellow.png
convert http://i.stack.imgur.com/XITAE.png yellow.png -compose colorize -composite colouredmask.png -compose overlay -composite result.png
Notes
Squashing pixels... sorry for confusing you with my terminology! Basically, when I squash the pixels down to a single row, you need to imagine dropping a brick on the top of the image and flattening it down to just one pixel tall. So, essentially, you draw an imaginary line underneath the image and then you work across the image totalling up the number of WHITE (i.e. edge) pixels in each vertical column. Columns that have more white pixels will add up to larger numbers. Columns that have no white pixels will add up to zero. Once you have got the totals for each column, you find the highest total - let's say it is 32 and then you multiply all totals by 255/32 so that everything is normalized to 255, or white. Now the squashed strip represents the edge energy in each column. And I then use that as the opacity for the red when I overlay - so columns with more white edges in the Canny image will show up with more red in the result.
Let's demo what happens if I squash down to 10 pixels wide and 1 pixel high before scaling back up to the original size - basically it means that my resulting mask will have only 10 possible values (or columns) columns and that each column will be a single constant brightness. I'll put the Canny image underneath so you can see that the brightness of the squashed strip represents the edge energy:
convert http://i.stack.imgur.com/XITAE.png -canny 0x1+15%+50% -resize 10x1! -normalize -scale 827x310! mask.png
If you want to introduce another colour, you need to work out what your algorithm is for controlling where that colour should appear. You then do exactly the same thing again - you make a mask that is light where you want that colour in your output image and dark where you don't want that colour. Then you use that mask as the opacity for your new colour (as I did at the line labelled line 2 above) and then you overlay it like I did in the last line of my code above.