A9 - Binary Operations

For this activity, I have a grayscale image of scattered circles. The goal is to obtain the most accurate measure of an area of a circle present in the image.



Since the image is too large to deal with, I first divide it into 256 x 256 subimages. And for each of these subimages, I used the algorithm below.


//Part A. Preparing the binary images to be used
im1 = imread('image.jpg'); // loads one of the subimages

se1 = imread('se.jpg'); // loads the structure element

im2 = im2gray(im1);
se2 = im2gray(se1);
im3 = im2bw(im2, 0.847 ); // 0.817 is the threshold value
se3 = im2bw(se2, 0.5);
// Part B. Image cleaning
d = dilate(im3,se3,[2,2]);
e = erode(d,se3,[2,2]);
//Area Calculation
[L,n] = bwlabel(e); //labels each component
for j=1:n
f = find(L==j);
reg_size = size(f,'*');
if reg_size <> 600
L(f) = 0;
end
end
imwrite(L,newimage.jpg');
for i=1:n;
v = find(L==i);
Area = size(v,2) // Gives the area in terms of pixel number
end;

Part A. Preparing Binary Images

The image can either be a true color or a grayscale image. The area we wish to obtain is in terms of the number of pixels, hence it will be easier if we convert each subimages to binary. In doing this, we need to know the threshold value. Although an average threshold value can be used in all the subimages, I opted to obtain a threshold value for each subimage. This is to ensure that each blob will not blend with the background. Plus a good threshold value can remove unwanted spots in the image.

Part B. Image Cleaning.

Aside from choosing a good threshold value, dilation and erosion operations can be applied. Dilation can be used to fill up small holes inside the region of interest or ROI while erosion can be used to remove unwanted spots and blots. Together they can smoothen out the surfaces of the ROI.

Part C. Area Calculation

In some of the blobs, cluttered circles overlapping each other are present. I chose to remove these types circles so that it will not interfere with data gathering. In this case, blobs with sizes less than 400 and greater than 600 will be removed. Labeling is also important since we need to output the size of each circle and not the entire ROI. After labeling, we can simply output the area which is just the pixel size of each circle. The data is shown below.


The numbers 1 to 12 are the assigned names of the 12 subimages. Using MS Excel, we can plot the frequency vs area as shown below.

The data above shows that there are 28 circles having an area of 555. 28 pixels. To verify this result, I obtained the diameters of each of the circle in the untreated image.

diameter = 27 (+- 1) pix

Area of a circle = 572.55 pix (+- 7.41 %)

Hence the error is 3.01 %. Thus we can conclude that the process shown above is an effective method of area computation.

Ed and Rica helped me in this activity.

I rate myself 10/10 for my effort in this activity.