blood cells counting by using python open cv

RAJIV GANDHI UNIVERSITY OF KNOWLEDGE
TECHNOLOGIES
MINI PROJECT-2
DEPARTMENT OF ECE
BLOOD CELLS COUNTING USING CV PYTHON

TEAM MEMBERS:
PROJECT GUIDE NAME: M.SNEHITHA (S190683)
MS.K.SUDHARANI K.VIJAYALAKSHMI(S190651)
ECE DEPARTMENT M.SAI TEJASWI(S190972)
A.RAGAMANEESHA(S190083)
P.D.H.SUBHASHINI(S190114)
G.KUSUMA(S190350)
BATCH NUMBER:19

Contents:
 Objective
 Abstract
 Introduction
 Literature review
 Tools required
 Flow chart
 Advantages
 Disadvantages
 Future Scope
 Conclusion

Objective
The aim of this project is to Analyzing the Blood Cells Counting using
Python OpenCV and predict output for White blood cell Red blood cell
and Platelets. The primary objective of this to present a more accurate
counting of blood cells using the python OpenCV programming
language. It covers image processing and analysis of platelets, red blood
cells and white blood cells.

Abstract:
Automated blood cell counting plays a crucial role in medical diagnostics and research. This project
focuses on developing a computer vision (CV) application using Python for counting different types
of blood cells in microscopic images. The process involves several key steps: image preprocessing to
enhance quality and reduce noise, segmentation to isolate individual cells, feature extraction to
characterize cells based on size, shape, and color, and finally, cell counting using algorithms like
watershed segmentation or deep learning-based approaches. The implementation utilizes popular
Python libraries such as OpenCV and scikit-image for image processing and analysis. The developed
system aims to accurately and efficiently count blood cells from microscopic images, offering
potential benefits in terms of time-saving and accuracy compared to manual counting methods.

Introduction:
Automated blood cell counting using computer vision (CV) and Python is a modern approach to
streamline diagnostic processes in medicine and research. Manual blood cell counting is labor
-intensive and prone to human error, highlighting the need for automated solutions. By harnessing
the power of image processing and machine learning, this project aims to develop a Python
-based system capable of accurately counting and classifying blood cells from microscopic
images. This report will detail the methodology and implementation of this automated blood cell
counting system, showcasing its potential impact on healthcare efficiency and diagnostic
accuracy.

Literature Review:
[1] Platelet count is one of the blood tests involved in the process of CBC to determine if the
patient suffers from anemia, leukemia and etc. Plate counting is usually done manually but a
recent study showed that this process can be done through Circular Hough Transform in a
microscopic blood cell images. This process presented an accuracy rate of 96% compared with
traditional manual counting.
[2] Traditional white blood cell counting is a long process and contributes some inaccuracy. If
more accuracy in white blood cell counting would like to obtain, an expensive haematological
analysing machine is needed. Hence, a study about microscopic images of blood stained
peripheral blood film for leukemia and normal condition was presented. It involves color space
conversion, color thresholding, filtering, marker controlled watershed and morphological
operations which got an accuracy of 88.57%.

[3] Detection and counting of white blood cells in blood samples were also presented through computer-
aided and mobile-cloud-assisted blood analysis. The paper propose a smartphone-based cloud-assisted
resource aware framework for localization of WBCs within microscopic blood smear images using a trained
multi-class ensemble classification mechanism in the cloud. Its algorithm includes segmentation, extraction
of texture, statistical, and wavelet features and then categorized into five classes: basophil, eosinophil,
neutrophil, lymphocyte, and monocyte. Counting each type of cells was then accomplished.
[4] Abnormalities in white blood cells were also studied by researchers through digital image processing.
The study presented is fast and inexpensive that can detect kind of diseases like Chronic Obstructive
Pulmonary Disease, Immune system disorders, Neutropenia, HIV/AIDS, Lymphocytopenia, leukemia etc.
There are two proposed framework presented in the paper. The first framework determined the types of
nucleus in WBC and the second framework is the counting of WBC and abnormal nucleus in the WBC. The
result showed more than 85% accuracy.

[5] Clinical decision support system for cells counting and classification is existing nowadays. A computer
aided system can simulate a human visual inspection to automate process of detection and
determination of WBCs and RBCs from blood sugar smears. This method has been tested on public
datasets of blood cell images and demonstrated a reliable and efficient system for differential counting.
The result obtained accuracy value of 99.2% for WBC and 98% for RBC.

Tools required:
 Jupyter notebook
 Colab

Upload image/s
The ten (10) square subdivision images of the
of the blood specimthe Python based program,
processed en were uploaded in and analyzed.
In order for a python program to process
image processing, OpenCV function must be
imported.
Images used in both WBC and RBC programs
were samples captured using 40x
magnification setting of a microscope while in
Platelet program, 100x magnification images
were used. High magnification was necessary
for platelet counting since among the three

Image enhancement
Image enhancement is the process of digitally manipulating a stored image using
software. The tools used for image enhancement include many different kinds of
software such as filters, image editors and other tools for changing various
properties of an entire image or parts of an image.
Some of the most basic types of image enhancement tools simply change the
contrast or brightness of an image or manipulate the grayscale or the red-green-
blue color patterns of an image. Some types of basic filters also allow changing a
color image to black and white, or to a sepia-tone image, or adding visual effects.

Image segmentation
In Image segmentation process, we first masked out the resulting HSV image to
separate objects from the background using a pixel feature value. In our study, we
used Otsu's binarization technique for thresholding purpose.
In this technique, it automatically calculates threshold values from the two peaks of
the histogram of a bimodal image using the formula. It actually finds a value of t
which lies in between two peaks such that variances to both classes are minimum.

Blob detection
A Blob is a group of connected pixels in an image that share
some common property (e.g grayscale value). The goal of
blob detection is to identify and mark these regions.
Blob detection provides methods for segregating those
samples by thresholding, grouping, merging and radius
calculation. Thresholding converts the source images to
several binary images by applying the source images the
threshold from minimum to maximum threshold. Grouping is
identifying binary images connected with pixels or binary
blobs. Merging is computing the center of the binary blob
located closer than minimum distant between blobs and the
last radius calculation by computing radii of the
new merge blobs.

Cell counting
Having successfully isolated the cells for RBC, WBC and Platelet cell counter, each
of the 10 images were process separately. The number of cells per image are
summed up and were accordingly configured to get the correct results which are
expected to achieved close to the expected text results if not the same.
In WBC Counter, the total sum of the cell counts from the ten images needs to be
multiplied with 0.1 to get the final WBC test results. Whereas for RBC Counter, the
total sum of the cell counts from the ten images needs to be multiplied with 0.001
to get the final WBC test results.

Advantages:
Efficiency
Consistency
Accuracy
Objective Analysis
High Throughput
Cost-Effective
Flexibility
Integration
Real-Time Analysis
Supports Research

Dis advantages:
Complex Implementation
Algorithm Tuning
Sensitive to Image Quality
Difficulty with Overlapping Cells
Limited Generalization
Initial Setup Cost
Maintenance Needs
Ethical Considerations

Future Scope:
1. Advanced Image Processing:
Enhanced noise reduction and deep learning models for better cell detection and
classification.
2. Real-Time Processing:
Implementation on portable devices and cloud computing for immediate and
remote analysis.
3. Automated Diagnostics:
AI models for disease detection and predictive analytics.

4.3D Imaging:
3D reconstruction and volumetric analysis for detailed cell morphology.
5.Multi-Modal Analysis:
Combining blood cell images with other diagnostic data and genomic
information.
6.User Interfaces:
Interactive tools and AR for improved pathologist interaction and manual review.

Conclusion:
The researchers presented more accurate blood cell counting using a new algorithm
with the help of python OpenCV programming language. The implementation of
Image processing and analysis for the platelet, red blood and white blood cells was
made possible and resulted to high level of accuracy.

blood cells counting by using python open cv

blood cells counting by using python open cv

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