Cell calculation

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

Overview

Objectives

Background

Matlab

Importing the Image

Thresholding

Inverse Mapping

Image Subtraction

Counting the Number of Cells

Conclusion

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

Background

Basic Thresholding

Background pixels have intensity values grouped into two dominant modes

Select a threshold, T, that separates these modes

Segmented image:

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(Gonzalez & Wood, 2008)

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


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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


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Background

Otsu’s Method Evaluate k using normalized, dimensionless metric

Introduce variable k into the previous equation

Obtain maximum threshold value k*, by maximizing σ2B, then apply to basic

thresholding equation

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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');

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

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');

Thresholding

Thresholded Image

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

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');

Inverse Mapping

Threshold Image-Color Inverted

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');

Image Subtraction

image after small pixels removed

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);

Image Subtraction

Image of the individual 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


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


Threshold Image-Color Inverted of the Original Tissue Section

Objects Found:668


Threshold Image-Color Inverted of the Repaired Tissue Section

Objects Found:1667

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)

References

Gonzalez, R.C, Woods, R.E., Digital Image Procesing, 3rd Edition, Pearson Prentice Hall, 2008.

Images obtained from bioinstrumentation lab UC

Engineering

Cell calculation