The document outlines a research proposal on detecting arthritis using thermal imaging. It discusses the literature surrounding using infrared thermography and machine learning to diagnose arthritis. The proposed methodology would involve collecting thermal images of knees, extracting statistical features, and using support vector machines for classification of images as normal or arthritic. The expected outcomes are more accurate detection of arthritis at earlier stages to improve treatment. The timeline outlines the stages of data collection, model development and testing over months.

1. INDEX
• Introduction
• Literature Survey
• Finding from Literature Survey
• Identified Research Gap
• Aims and objectives
• Proposed Methodology
• Expected Outcome
• Title of Research
• Plan of Research Work
• References
Department of Electronics and Telecommunication
Outline of the seminar

2. Thermal imaging with infrared cameras expands the
"visible" spectrum of the human eye by doing the
work an eye cannot. It perceives these longer
wavelengths and captures them in a color-coded
world that the human eye can understand.
Everything in the world with a temperature above
that of absolute zero emits some level of heat
which can be detected and measured.
Infrared Thermography Principle
Infrared thermography is defined as an equipment
which detects infrared energy emitted from an
object, converts it to temperature, and displays
the image of the temperature distribution
Department of Electronics and Telecommunication
Introduction

3. Arthritis is a disease that affects your joints (areas where your bones meet and
move). Arthritis usually involves inflammation or degeneration (breakdown)
of your joints. These changes can cause pain when you use the joint.
Arthritis is most common in the following areas of the body: Feet. Hands. Hips.
Knees. Lower back.
What are the different types of arthritis?
Arthritis is a broad term that describes more than 100 different joint conditions.
The most common types of arthritis include:
Osteoarthritis, or “wear and tear” arthritis, which develops when joint cartilage
breaks down from repeated stress. It’s the most common form of arthritis.
Gout, a disease that causes hard crystals of uric acid to form in your joints.
Psoriatic arthritis, joint inflammation that develops in people with psoriasis
(autoimmune disorder that causes skin irritation).
Rheumatoid arthritis, a disease that causes the immune system to attack synovial
membranes in your joints.
Department of Electronics and Telecommunication

4. Thermal imaging applications abound in the field of
healthcare, both for humans and animals. Infrared
thermography in thermography is being used to help
detect cancer earlier, locate the source of arthritis, and
even catch circulation issues before they become too
problematic. Doctors and veterinarians alike can use
infrared cameras to discover muscular and skeletal
problems early on
Department of Electronics and Telecommunication
Application of Thermography

5. Name of
Author
Year of
Publication
Paper Name Name of Journal Abstract
Jenny Ann
Verghese1,2, D.
Pamela1,2, Prawin
Angel Michael2
2021 Rheumatoid
arthritis
detection using
image
processing
Journal of Physics:
Conference Series
This automated system
requires clear Xray
images, which after
preprocessing and
segmentation using
Support Vector Machine
implemented via
MATLAB gives a clear
classification about the
abnormal and normal
images
Utkarsh Vikram
Singh; Eva Gupta;
Tanupriya
Choudhury
2019 Detection of
Rheumatoid
Arthritis Using
Machine
Learning
IEEE In this research paper,
machine learning
algorithms are
implemented to predict
rheumatic arthritis (RA)
by the help of the four
factors for the study of
rheumatic diseases
Department of Electronics and Telecommunication
Literature Survey

6. Name of Author Year of
Publication
Paper Name Name of Journal Abstract
Parijata
Majumdar ,
Kakali Das ,
Niharika Nath ,
Mrinal Kanti
Bhowmik
2018 Detection of
Inflammation
from
temperature
profile using
Arthritis knee
joint Datasets
IEEE International
Conference on
Healthcare
Informatics
offers an insight to the
determination of
severity of the disease.
In this scope, author
validate the importance
of infrared imaging with
a newly created
datasets of Arthritis
knee joints. After
validation, the efficacy
of infrared imaging is
also proved as a
complementary
diagnostic tool to other
clinical tests in detecting
inflammation that lacks
recognizable clinical
findings in relation to
Arthritis.
Department of Electronics and Telecommunication
Literature Survey

7. Name of Author Year of
Publication
Paper Name Name of Journal Abstract
Małgorzata
Gizińska,
Radosław
Rutkowski,
Lucyna
Szymczak-Bartz,
2018 Thermal
imaging for
detecting
temperature
changes within
the rheumatoid
foot
Spinger Journal of
Thermal Analysis
and Calorimetry
study reports the
development of a thermal
imaging method suitable
for the screening and
differentiation of joint
inflammation in the
rheumatoid foot of
patients in comparison
with the control group of
healthy participants.
Berend C.Stoel 2019 Artificial
intelligence in
detecting early
RA
Elsevier Seminars
in Arthritis and
Rheumatism
an overview is given on
the background and
history of artificial
intelligence, with a special
focus on recent
developments in ‘deep
learning’, and how these
techniques could be
applied to detect subtle
inflammatory changes in
MRI data.
Department of Electronics and Telecommunication
Literature Survey

8. Department of Electronics and Telecommunication
Finding from Literature Survey
• Selecting the X-ray scan as a diagnosis tool will expose the
patient to excessive radiation which can contribute to major
side effects such as an increased risk of Due to side effects of
radiation, doctors would prefer the use of other alternative
imaging methods. Ultrasonography is an alternative to the X-
ray scan; however, it is largely avoided by doctors due to
poor visual representation and lack of reliability
• Recent studies exploring the possibility of using artificial
intelligence (AI) in diagnosis of OA have used the ultrasound
to increase the detection accuracy. Magnetic resonance
imaging (MRI) provides a much more accurate visual
representation of the cartilage structure. However, the cost
of the test and facility required for the MRI OA diagnosis
make this option not suitable for majority of patients
especially in urban areas

9. Department of Electronics and Telecommunication
Identified Research Gap
• observed colour pattern depends on the prevailing
temperature of the target in a controlled environment.
This colour-based thermal pattern is further processed
for identifying abnormalities. This process o
identification is done
• These steps are applied to thermal images abnormalities
were identified

10. Department of Electronics and Telecommunication
Aims and objectives
• ongoing research for detecting and diagnosing Various Arthritis, which aims to reduce
the rate of occurrence of the disease and detect it in its earlier stages in order to treat it
prior to its growth and development. However, this provides different and additional
methods and techniques to reach the desired purpose which is to classify it into three
main classes: Normal (no OA) or abnormal (arthritic knee). This is done using two main
phases: image processing and neural network through which the images are processed
then classified using SVM.
• OA is a dangerous and chronic disease that should be analyzed and detected in its early
stages. Thus, the aim of this thesis is to develop a new approach for the identification of
osteoarthritis through knee thermal image processing techniques and support vector
machine classifier. Thus, supplied knee image must be classified either normal or
abnormal. The proposed system uses thermal knees images obtained from a created
database images for testing phase.
• The image processing techniques used facilitates the diagnosis of that disease by
analyzing and pointing out the osteoarthritis signs and symptoms through extracting the
useful and needed features or patterns. Moreover, the developed system helps the
doctors to accurately classify the OA knee infrared thermal images since it is designed to
stimulate the human visual inspection that is based on visualizing som e related features
and signs of OA particularly for this project involves only software which is Matlab. This
software will be used to develop a program for detection and
classification of osteoarthritis

11. Proposed Methodology

12. Department of Electronics and Telecommunication
Hardware and Software
Requirement specifications
• Laptop/Desktop
• Matlab
• SVM(support vector machine) Toolbox

13. Department of Electronics and Telecommunication
Feature Extraction
• The infrared camera and other thermal imager detect changes in skin temperature of the subject by
continuously monitoring the modulation (i.e., increase or decrease) of skin temperature. In this research,
‘Infrared Camera based temperature profiles have been acquired from face and ear, buccal cavity etc.
during Diabetic Camps and cancer patients for pilot studies on the subjects. The IR camera (FLIR SC325) is
used to capture and analyse the report generated from above experiments.
• For preprocessing of thermal image we will used different types of filter like median, lee, or frost filter
• Infrared Image processing techniques here we will use FCM segmentation technique The thermal
information extraction for certain application was the main challenge here for selection of proper region
of interest (ROI) from infrared images for fulfilment of medical purposes. The region of interest (ROI) can
be extracted by following three manners: a) Manual ROI selection, b) Semiautomated ROI selection and c)
Full-automated ROI selection. To obtain Full-automated ROI selection, Manual ROI selection is necessary
for the first time to save the ear templates. In this case seven ear templates are cropped by selective
manner from eighty five subjects. The saved templates are acting as feature for image registration. For
semi-automated ROI selection software provides a region where possible coordinates of ear zone may be
visible. The user has to accept if the selection is correct. In this manner, three ear templates are selected.
Total 10 templates are used here for full-automated ROI selection module.
• A statistical image analysis algorithm has been included in the "infrared image analysis module" where
Mean, Standard Deviation, Median, Mode, Skewness, Kurtosis, First, Second , Third order Moment, Root
Mean Square (RMS), Norm Entropy, Shanon Entropy, Energy and Maximum temperature value of the
extracted
• The statistical features from the extracted thermal array are further used for machine learning algorithm
where best three features are extracted by feature ranking algorithm and trained by SVM learning
algorithm for classification and analysis. In the intermediate stages of software development other
classification algorithms

14. Department of Electronics and Telecommunication
Expected Outcome

15. • Monthly Schedule
15
Schedule
Month Description
Aug, 2020
Sept / Oct, 2020
Nov / Dec, 2020
Jan ,2021
Feb / March, 2021
April ,2021

16. Department of Electronics and Telecommunication
References
• Jenny Ann Verghese1,2, D. Pamela1,2, Prawin Angel Michael2 ,” Rheumatoid arthritis detection using
image processing , “,2021 Journal of Physics: Conference Series
• Utkarsh Vikram Singh; Eva Gupta; Tanupriya Choudhury “Detection of Rheumatoid Arthritis
Using Machine Learning “,2019 IEEE
• Parijata Majumdar , Kakali Das , Niharika Nath , Mrinal Kanti Bhowmik ,"Detection of Inflammation from
temperature profile using Arthritis knee joint Datasets ",2018, IEEE International Conference on
Healthcare Informatics
• Małgorzata Gizińska, Radosław Rutkowski, Lucyna Szymczak-Bartz, “Thermal imaging for detecting
temperature changes within the rheumatoid foot “,2018, Spinger Journal of Thermal Analysis and
Calorimetry
• Berend C.Stoel ,”Artificial intelligence in detecting early RA”. ,2019Elsevier Seminars in Arthritis and
Rheumatism
• Asok Bandyopadhyay , Amit Chaudhuri , Himanka Sekhar Mondal" IR Based Intelligent Image Processing
Techniques for Medical Applications" Rinsho Byori. IEEE 2016 Feb;53(2):113-17.
• Shawli Bardhan, Satyabrata Nath, Tathagata Debnath, Debotosh Bhattacharjee and Mrinal Kanti
Bhowmik "Designing of an Inflammatory Knee Joint Thermogram Dataset for Arthritis Classification Using
Deep Convolution Neural Network", Quantitative InfraRed Thermography Journal (QIRT), Taylor &
Francis Online, I
• Faisal, A., Ng, S.-C., Goh, S.-L., & Lai, K. W. (2017). Knee cartilage segmentation and thickness
computation from ultrasound images. Med Biol Eng Comput. doi:10.1007/s11517-017-1710-2.
• [Danu Abraham, A. M., Goff, I., Pearce, M. S., Francis, R. M., & Birrell, F. (2011). Reliability and validity of
ultrasound imaging of features of knee osteoarthritis in the community. BMC Musculoskeletal Disorders, 12,
70. http://doi.org/10.1186/1471-2474-12-70
• Brenner, G. A., Darby, S. (2004). Risk of cancer from diagnostic X-rays: estimates for the UK and 14 other
countries. Lancet, 363(9406), 345-351. doi: 10.1016/s0140- 6736(04)15433-0.