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Another example – morphological operations
Read the stock image “rice.png”: rice=mat2gray(imread(‘rice.png’));
View the image, notice the background is not even: imtool(rice,[]);
We will “open” this image, using the function imopen, to remove
the small features and get a look at the background.
‐imopenis a morphological erosion followed by a dilation
‐imcloseis the reverse
As an example, load the image “sticksandballs.tif”
‐We can use the imopenfeature to remove the sticks  
‐Try opening this image and removing the sticks with a disc 
shaped structural element (strel) 
sb_open=imopen(sb,strel(‘disk’,5));
Now let’s return to our unevenly illuminated rice:
‐open the image such that only the background remains
rice_open=imopen(rice,strel(‘disk’,20));
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Even rice… yeah baby!
Look at the original image with the uneven background subtracted
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Even rice… yeah baby!
Look at the original image with the uneven background subtracted
‐rice_nobg=rice‐rice_open
‐imshow(rice_nobg)
But we aren’t using the entire contrast range now, adjust the image
‐rice_nobg=imadjust(rice_nobg);
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Even rice… yeah baby!
Look at the original image with the uneven background subtracted
‐rice_nobg=rice‐rice_open
‐imshow(rice_nobg)
But we aren’t using the entire contrast range now, adjust the image
‐rice_nobg=imadjust(rice_nobg);
Segmentation is a common need in biology image processing; let’s segment the rice using a 
simple threshold as a criterion for what is rice and what is not. 
‐imhistgives a histogram of the intensity of the image pixels
‐chose a threshold between the background (around 0) and the rice (around 1)
‐Using im2bwmake the pixels below the threshold black and the ones above white
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Even rice… yeah baby!
Look at the original image with the uneven background subtracted
‐rice_nobg=rice‐rice_open
‐imshow(rice_nobg)
But we aren’t using the entire contrast range now, adjust the image
‐rice_nobg=imadjust(rice_nobg);
Segmentation is a common need in biology image processing; let’s segment the rice using a 
simple threshold as a criterion for what is rice and what is not. 
‐imhistgives a histogram of the intensity of the image pixels
‐chose a threshold between the background (around 0) and the rice (around 1)
‐Using im2bwmake the pixels below the threshold black and the ones above white
rice_bw=im2bw(rice_nobg,0.6);
Each connected region of 1’s can be given it’s own label with bwlabel.
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Even rice… yeah baby!
Look at the original image with the uneven background subtracted
‐rice_nobg=rice‐rice_open
‐imshow(rice_nobg)
But we aren’t using the entire contrast range now, adjust the image
‐rice_nobg=imadjust(rice_nobg);
Segmentation is a common need in biology image processing; let’s segment the rice using a 
simple threshold as a criterion for what is rice and what is not. 
‐imhistgives a histogram of the intensity of the image pixels
‐chose a threshold between the background (around 0) and the rice (around 1)
‐Using im2bwmake the pixels below the threshold black and the ones above white
rice_bw=im2bw(rice_nobg,0.6);
Each connected region of 1’s can be given it’s own label with bwlabel.
rice_label=bwlabel(rice_bw);
‐View the resulting image
‐Find the area and perimeter of each labeled region
with regionprops
Even rice… yeah baby!
Look at the original image with the uneven background subtracted
‐rice_nobg=rice‐rice_open
‐imshow(rice_nobg)
But we aren’t using the entire contrast range now, adjust the image
‐rice_nobg=imadjust(rice_nobg);
Segmentation is a common need in biology image processing; let’s segment the rice using a 
simple threshold as a criterion for what is rice and what is not. 
‐imhistgives a histogram of the intensity of the image pixels
‐chose a threshold between the background (around 0) and the rice (around 1)
‐Using im2bwmake the pixels below the threshold black and the ones above white
rice_bw=im2bw(rice_nobg,0.6);
Each connected region of 1’s can be given it’s own label with bwlabel.
rice_label=bwlabel(rice_bw);
‐View the resulting image
‐Find the area and perimeter of each labeled region
with regionprops
rice_dims=regionprops(rice_label, ‘Area’, ‘Perimeter’);
Segmented rice
Now we can answer questions like:
What is the mean area of the rice? The variance in the mean? The perimeter?
Segmented rice
Now we can answer questions like:
What is the mean area of the rice? The variance in the mean? The perimeter?
areas=[rice_dims.Area]
mean(areas)
var(areas)
hist(areas)
and on and on
For fun, try segmenting the supplied image “fish.tif”
Point Spread Function
•For an ideal optical 
system, a point source
is not a point at the 
image plane.
•Instead, it appears as a 
Point Spread Function
(PSF) – aka the impulse 
response function for 
the optical system.
Put 
experimental 
one here
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