Cell calculation
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This document outlines a method for segmenting and counting cells from digital microscopy images using thresholding techniques in MATLAB. It describes the processes of image import, thresholding, inverse mapping, and the subsequent counting of cells, along with potential errors in counting due to staining and image artifacts. The conclusion indicates that the algorithm successfully shows a higher cell count in repaired tissue compared to original tissue, with future work suggested for improving accuracy and automation.
![Background
Otsu’s Method
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Cell calculation
- 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');
- 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