One of the important application in image processing is to be able to judge the different edges in an image and be able to distinguish various parts of it. Clearly, this is an application dependent task and results and analysis will vary as per the situation. These slides show a general description on how to calculate the number of cells in a microscopic tissue. There are two version one being the over-stained tissue and the other being the better one. We see how the algorithm is able to calculate the number of cells.

1. A N I R U D H M U N N A N G I
D A V I D D R A K E
Thresholding and Counting

2. Overview
 Objectives
 Background
 Matlab
 Importing the Image
 Thresholding
 Inverse Mapping
 Image Subtraction
 Counting the Number of Cells
 Conclusion

3. Objectives
 Segment cells from the background media in a
digitized microscopy slide image using a basic
thresholding technique
 Count the total amount of cells segmented from the
background media

4. Background
 Basic Thresholding
 Background pixels have intensity values grouped into two
dominant modes
 Select a threshold, T, that separates these modes
 Segmented image:
Tyxfif
Tyxfif
yxg
),(
),(
0
1
),(
(Gonzalez & Wood, 2008)

5. Background
 Global Thresholding
1. Select an initial estimate for the global threshold, T
2. Segment the image using the previous equation
3. Compute the average (mean) intensity values m1 and m2 for
the pixels G1 and G2, respectively
4. Compute a new threshold value:
5. Repeat steps 2 through 4 until optimum threshold
determined
(Gonzalez & Wood, 2008)
)(
2
1
21 mmT

6. Background
 Otsu’s Method
 Digital image with M x N pixels has {0, 1, 2, …., L – 1} Intensity Levels
 Normalized histogram
 T(k) = k, 0 < k < L-1; separate pixels into two classes C1 and C2
 Probability that a pixel is assigned to class C1 and C2
 The mean intensity value of the pixels assigned to C1 and C2
(Gonzalez & Wood, 2008)
NMnp ii / 0,1
1
0
i
L
i
i pp
k
i
ipkP
0
1 )( )(1)( 1
1
1
2 kPpkP
L
ki
i
k
i
iip
kP
km
01
1
)(
1
)(
1
12
2
)(
1
)(
L
ki
iip
kP
km

7. Background
 Otsu’s Method
 Evaluate k using normalized, dimensionless metric
 Introduce variable k into the previous equation
 Obtain maximum threshold value k*, by maximizing σ2
B, then apply to basic
thresholding equation
2
2
G
B
)1(
)(
11
2
12
PP
mPmG
B
1
0
2
.
2
)(
L
i
iGG pmi
2
2
)(
)(
G
B k
k
))(1)((
)]()([
11
2
12
kPkP
kmkPmG
B
(Gonzalez & Wood, 2008)
)(max*)( 2
10
2
kk B
Lk
B
*),(
*),(
0
1
),(
kyxfif
kyxfif
yxg

8. Importing the Images
 In Matlab:
img_gs=imread('Normal1.jpg'); %Load the image
figure
imshow(img_gs); %Plot the original image
title('Original Image: Grayscale');

9. Importing the images
X: 349 Y: 270
Index: 173
RGB: 0.529, 0.529, 0.529
Original Image: Grayscale
X: 456 Y: 546
Index: 171
RGB: 0.514, 0.514, 0.514
X: 746 Y: 388
Index: 176
RGB: 0.553, 0.553, 0.553

10. Thresholding
 In Matlab:
ith=175/255; %Determine threshold by cursor
img_t=im2bw(img_gs,th); %Convert to binary
figure
imshow(img_t); %Plot the image
title('Thresholded Image');

11. Thresholding
Thresholded Image

12. Thresholding
 Due to cell staining and digitization of the
microscopy slides, the corners of the slides were
darkened towards the color of the individual cells
 This will cause tremendous error in our counting
algorithm, so it must be removed using inverse
mapping and image subtraction

13. Inverse Mapping
 In Matlab:
imginv=~img_t; %Take the inverse of the binary map
figure
imshow(imginv); %Plot the inversed binary image
title('Threshold Image-Color Inverted');

14. Inverse Mapping
Threshold Image-Color Inverted

15. Image Subtraction
 In Matlab:
%Find large connected parts
subimg=bwareaopen(imginv,400);
%Best approximation of threshold is 400 after trial and error
figure
imshow(subimg); %Plot the image
title('image after small pixels removed');

16. Image Subtraction
image after small pixels removed

17. Image Subtraction
 In Matlab:
%Subtract the corners from the inverse binary map
newimg=imginv-subimg;
figure
imshow(newimg); %Plot the image
title('Image of the individual cells);

18. Image Subtraction
Image of the individual cells

19. Counting the Number of Cells
 Important to approximate the exact number of cells
 Results will be used in a statistical analysis comparing repaired
tissue with controls
 Counting error will most likely occur
 How much fluorescence is absorbed by the cells determines
the appropriate threshold parameter (some cells excluded)
 Elimination of the corner areas (more exclusion of cells)
 Each slide differs in the amount of staining

20. Counting the Number of Cells
 In Matlab:
%Count the number of cells by finding groups of largely
%connected pixels
b=bwboundaries(img_t);
figure
hold on
imshow(img_t); %Plot the image
title('Threshold Image-Color Inverted');
text(10,10, strcat('color{green}Objects Found:', …
num2str(length(b)))); %Add count no. to image
hold off

21. Counting the Number of Cells
Threshold Image-Color Inverted of the Original Tissue Section
Objects Found:668

22. Counting the Number of Cells
Threshold Image-Color Inverted of the Repaired Tissue Section
Objects Found:1667

23. Conclusion
 Algorithm successfully thresholds and counts cells
from a digitized microscopy slide, thus able to show
that repaired tissue sections show more cells than
original tissue sections
 Future work
 Approximation of the cell count percent error
 Automation for calculating the threshold parameter
(i.e Global thresholding)
 Applying different thresholding algorithms
(i.e. Otsu’s method)

24. References
 Gonzalez, R.C, Woods, R.E., Digital Image
Procesing, 3rd Edition, Pearson Prentice Hall, 2008.
 Images obtained from bioinstrumentation lab UC