This document summarizes a student project presentation on developing a blood group detection system using image processing. The project is being conducted by 4 students in their 7th semester of Computer Science engineering under the guidance of a professor. The presentation includes an abstract, objectives, introduction on blood groups and their importance, scope of the project, literature survey of existing methods, problem definition, proposed work including the system requirements and references. The proposed work is to use a non-invasive approach involving image sensors and spectroscopic data to determine blood groups quickly and accurately in emergencies.

1. C BYREGOWDA INSTITUTE OFTECHNOLOGY
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Phase-1 Project Presentation
ON
“BLOOD GROUP DETECTION USING IMAGE PROCESSING”
Presented By : Chethan S G (1CK19CS028)
Girish K A (1CK19CS034)
Madhu Kiran G (1CK19CS046)
Mahanthesh A V (1CK19CS048)
BE CSE, 7th Sem
Under the Guidance of : Prof. Kavitha N
Assistant Professor,
Dept of CSE.

2. ABSTRACT

3. OBJECTIVE

4. INTRODUCTION
Blood is an essential to life. It circulates through human body and brings oxygen and nutrients
to all the parts of body so that they can keep working.
It carries carbon dioxide and other waste material to the lungs, kidneys and digestive system so
that waste material to be removed from the system.
 Blood group is a classification of blood based on the presence or absence of antigenic
substances in blood cells.
Blood Group abnormalities cause several blood diseases, and lead to fatal and chronic health
problems, including heart attack, stroke, and pregnancy complications.
 When an adequate blood is not maintained, the disorder complicates the function of the major
organs (eg, kidney, brain, and heart) that require oxygen.
Thus, an disorder diagnosis based on an invasive method is not a perfect solution, especially for
people in low- and middle-income countries.

5. SCOPE

6. LITERATURE SURVEY
Sl.no Title Author Methodology Advantages Disadvantages
[1] Classification of
blood types by
Microscopic color
images.
S.M. Nazia
Fathima.
This paper
concerns with the
ABO and Rh
blood typing
systems. The
classification of
blood types in
microscopy
images allows
identifying the
blood groups and
Rh factor
accurately.
corresponding
person’s blood
group can be
analyzed by
using
SVM(support
vector machine).
In this system
more skilled
persons are
needed to
handle the
system and it
is tedious to
do.

7. Sl.no Title Authors Methodology Advantages Disadvantages
[2] Improvement of
accuracy of Human
blood groups
determination
using image
processing
techniques.
Mehedi Talukdar,
Md Rabiul Islam,
Md. Mahfouz
Reza, Mahbuba
Begaum, Md.
Mahmudul Hasan.
The main
objective is to
present a
methodology to
determine human
blood groups using
image processing
techniques.
This will
contribute to
undertake safe
blood transfusions
and to reduce the
loss of human
lives.
The experts or
knowledge
persons
needed to
analyze the
plate method
that is used in
the system.

8. Sl.no Title Authors Methodology Advantages Disadvantages
[3] Real time blood
type
determination by
gel test method on
embedded system.
Enes Ayan,
Erdem Kamil
Yildirim.
It utilizes the
image processing
techniques and
gel test method
for real time
blood type
determination on
embedded
system.
Blood cells
which don’t
agglutinate
passes through
this gel to get
results and to
read this gel test
card a software is
developed.
Gel test
method needs
three devices
these are gel
test
centrifuge,
gel test
incubator and
gel test
reader, these
devices are
very
expensive.

9. Sl.no Title Author Methodology Advantages Disadvantages
[4] Determination
and classification
of blood types
using image
processing
techniques.
G. Ravindra, T.
Jonny Titus, M.
Pravin, P.
Pandiyan.
Thus, the
developed
automated
method
determines the
blood type using
image processing
techniques.
The developed
method is useful
in emergency
situation to
determine the
blood group
without human
error.
Only experts
can tell the
blood type by
seeing at the
agglutination
process

10. Sl.no Title Authors Methodology Advantages Disadvantages
[5] Blood group
detection by using
raspberry Pi-3
Anurag Sadashiv
Phad, Tejas
Sanjay Targhale,
Bharath
Bhalshankar,
Sunitha Kulkarni
This paper
presents a new
methodology for
determine blood
group by taking
image of blood
sample content
added chemical,
such as
Anitcolnal A, B,
D and by
processing this
image raspberry
pi 3 we can get
the blood group.
Processing of
image is carried
out such as
Morphology,
Thresholding,
Segmentation,
Quantification
etc.
Finally the
result is
display on the
LCD, But the
limitation of
the system is
it require
costlier
hardwires.

11. Sl.no Title Authors Methodology Advantages Disadvantages
[6] Blood group
determination
through medical
image processing.
Manish K,
Hitashree M,
Chandana
Lakshman
Hegde.
The point of this
frame work is to
give the outcome
inside the briefest
conceivable time
without the
human mistakes
utilizing image
processing
procedures.
In this
application the
main steps are
image
segmentation,
Thresholding,
Morphology,
Histogram and
Quantification.
Image
processing
technique
which can be
used by lab
technician
and novice
user with no
prior
knowledge to
blood group
detection.

12. PROBLEM DEFINITION
To implement Blood Group detection system by using microscopic image of blood sample. In this
project, it is very important to determine the blood type quickly and accurately in an emergency before
transfusion. Today, rapid blood typing methods based on image recognition technology are widely
used in automated blood analyzers.
This project proposes a fast, accurate, and robust blood group analysis method based on the imaging
function of the ABO high-speed blood group analyzer. Then, according to the gray level distribution
of the image, the characteristic parameters of the ABO blood group are extracted. With the
agglutination reaction between the antigen and the antibody, the system ultimately determines the
blood type. Experimental results show that this method can quickly and accurately classify ABO blood
groups.

13. PROPOSED WORK
 A noninvasive (without blood sample collection) approach involves data obtained from image sensors,
spectroscopic information, and output of a photoplethysmography (PPG) sensor to calculate the Blood
Group level.
 A Image Processing-based POC tool as a potential alternative to invasive clinical blood testing is rapidly
attracting attention because of the advantages of availability, user-friendliness, and easy attachability to
different biosensing devices.
 The fingertip area is one of the best data collection sites from the body, followed by the lower eye
conjunctival area.
 Near-infrared (NIR) light-emitting diode (LED) light were identified as potential light sources to receive a
Blood Group response from living tissue.
 PPG signals from fingertip videos, captured under various light sources, can provide critical physiological
clues.
 The features of PPG signals captured under NIR LED are considered to be the best signal combinations
following a dual-wavelength theoretical foundation

14. SYSTEM REQUIREMENTS

15. References
 Melur K. Rama Subramanian & Stewart P. Alexander, “An integrated fibreoptic–microfluidic device for
agglutination detection and blood typing” Biomed Microdevices, Sept. 2009, pp. 217–229[1]
 Faraz, V. Moreira, D. Silva, V. Carvalho and F. Soares, “Automatic system for blood type classification using image
processing techniques”, Biodevices 2011, Rome, Italy, 26-29 January 2011[2]
 National Institute of Medical Emergency. INEM, Available at < http://www.inem.pt > Accessed on:16-04-2012[3]
 Method of human blood types determination: http://www.prof2000.pt/users/csilvana/Metod.html. Accessed in June
2012[4]
 Jose Fernandes, Sara Pimenta, Filomena O. Soares and Graco Minas, “A Complete Blood Typing Device for
Automatic Agglutination Detection Based on Absorption Spectrophotometry”, IEEE Transactions on
Instrumentation and Measurement, 2014[5]
 Mouad.M.H.Ali, Vivek H. Mahale, Pravin Yannwar, and A. T. Gaikwad, “Fingerprint Recognition for Person
Identification and Verification Based on Minutiae Matching”, 2016 IEEE 6th International Conference on
Advanced Computing, Feb. 2016[6]
 G. Ravindran, T. Joby, M. Pravin, and P. Pandiyan, “Determination and Classification of Blood Types using Image
Processing Techniques,” International Journal of Computer Applications, vol. 157, no. 1, pp. 12–16, Jan. 2017[7]
 A. Narkis Banu, V. Kalpana, “An Automatic System to Detect Human Blood Group of Many Individuals in a
Parallel Manner using Image Processing”, International Journal of Pure and Applied Mathematics, vol-118, pp.
3119-3127, 2018[8]