microscopic image cell counting using the MATLAB

1. Showcased by:
Hussain S
Enroll no. 2021BTBME009
Presented to
Dr. Manas Nag
An introduction to Microscopic Image
Analysis for Cell Counting using MATLAB
1/12

2. Introduction
Microscopic image analysis has become an
indispensable tool in various fields, including
biology, medicine, and materials science. One
crucial application is cell counting, which plays a
significant role in understanding cellular behaviors,
disease diagnosis, and drug development. MATLAB
provides a versatile platform for implementing
sophisticated image processing algorithms
efficiently. This introduction aims to provide a
foundational understanding of microscopic image
analysis for cell counting using MATLAB, covering
fundamental concepts, techniques, and practical
implementations.
2/12

3. Basic Principles of Cell Counting:
Manual Counting: Involves visually
counting cells using a microscope
and a counting chamber such as a
hemocytometer or a counting grid.
Automated Counting: Utilizes
specialized instruments such as
flow cytometers, Coulter counters,
and automated cell counters that
employ image analysis or electrical
impedance techniques to count
cells quickly and accurately. 3/12

4. Applications of Cell Counting:
Medical Diagnosis and Research:
Monitoring disease progression (e.g., leukemia).
Studying cell proliferation, differentiation, and
apoptosis.
Drug Development and Screening:
Assessing compound toxicity on cells.
Evaluating drug efficacy in vitro.
Biotechnology and Bioprocessing:
Monitoring cell cultures in fermentation processes.
Optimizing product yields in pharmaceutical and biofuel
production.
4/12

5. Agricultural and Environmental Sciences:
Assessing plant cell health and viability.
1.
2. Monitoring microbial populations in soil, water, and air.
Food and Beverage Industry:
Controlling yeast and bacterial populations in
fermentation.
1.
2. Ensuring product consistency and safety standards.
5/12

6. Preparation of Microscopic Images: First, ensure you have high-
quality images of your microscopic samples. These images should be
clear and well-focused to facilitate accurate analysis.
Image Preprocessing: Before starting the cell counting process,
preprocess the images to enhance the quality and make it easier to
detect cells. Common preprocessing steps include:
Image resizing: Ensure all images are of the same size for consistency.
1.
2. Image denoising: Remove noise from the images using filters like
Gaussian or median filter.
3. Image enhancement: Enhance the contrast of the images to make
cells stand out more clearly.
Steps to perform microscopic image analysis for cell
counting using MATLAB:
6/12

7. Segmentation: Segmentation is the process of partitioning an image into
meaningful regions. In this case, we want to segment the cells from the
background. You can use various segmentation techniques such as
thresholding, edge detection, or machine learning-based approaches. The goal
is to separate the cells from the background as accurately as possible.
Cell Counting: Once the cells are segmented, you can count them using
various methods. One simple method is to count the number of segmented
regions or objects in the image.
MATLAB provides functions to count the cell is:
e=length(r)
f=sprintf('The normal red blood cell count is %d',e)
disp(f)
to find connected components in a binary image, which can be used for
counting cells.
7/12

8. Validation and Refinement: After counting the cells, it's
essential to validate the results to ensure accuracy. You
can manually verify a subset of the counted cells to see if
they match the automated count. If discrepancies are
found, refine your segmentation or counting algorithms
accordingly.
Reporting Results: Finally, present your results in a clear
and understandable manner. This could include statistics
such as total cell count, cell density, and any relevant
measurements or analyses. Visualizations such as
histograms or scatter plots can help illustrate your
findings effectively.
8/12

9. clc;
clear all;
close all;
a=imread("C:UsersCSELAB112DesktopImage-2.png");
figure;imshow(a);
title('rbc original image');
% Convert to grayscale
b=rgb2gray(a);
figure;imshow(b);
title('gray image');
%edge detection using canny
c=edge(b,"canny",0.1);
figure;imshow(c);
title('edge detection using canny');
Program with Result
9/12

10. %measuring distances on image
d=imdistline;
[c r]=imfindcircles(c,[10,90]);
figure;imshow(a);
hold on
%Displaying circles on an image
viscircles(c,r,'color','b');
e=length(r)
title('Displaying circles and counting the cell rbc ');
%formatted data to a file
f=sprintf('The normal red blood cell count is %d',e)
disp(f)
The normal red blood cell count is 167
f = 'The normal red blood cell count is 167'
Final output Result
10/12

11. In conclusion, MATLAB's image analysis accurately counts cells, revealing spatial
variations. This method, vital for biomedical research, offers insights into cellular
behavior and interactions, facilitating advancements in understanding biological
processes and disease mechanisms.
Conclusion:
11/12

12. Thank
Thank
Thank
you!
you!
you!
12/12