book

Artificial Intelligence-based Infrared Thermal Image Processing and its Applications

Name: Artificial Intelligence-based Infrared Thermal Image Processing and its Applications
ISBN: 9781000688450

by U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer

September 2022

Intermediate to advanced

246 pages

8h 11m

English

CRC Press

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Cover Page
Half Title page
Title Page
Copyright Page
Contents
Preface
Acknowledgments
Authors
Chapter 1 Fundamentals of Infrared Thermal Imaging
1.1 Basics of Thermometry1.2 Thermal Properties of Biological Tissues1.3 Thermal Signals Associated with Physiological Functions1.3.1 Temperature Signal of Respiration1.3.2 Thermal Heart Rate Signal1.3.3 Identification of Sweat1.3.4 Facial Muscle Activation Signals1.4 Examples of Recent Applications of Infrared Thermal Imaging in Medicine1.4.1 Rheumatoid Arthritis1.4.2 Diabetes Mellitus1.4.3 Dermatology1.4.4 Dental Disorders1.4.5 Complex Regional Pain Syndrome1.4.6 Fever Screening1.5 SummaryReferences
Chapter 2 Protocol for Standardized Data Collection in Humans
2.1 Background and International Guidelines2.2 Thermal Camera Performance Check2.3 Ambient Temperature Control2.3.1 Choice of Ambient Temperature2.3.2 Structure of the Examination Space2.4 Subject Selection2.4.1 Procedures Performed during the Examination2.4.2 Behavior Prior to the Thermal Imaging2.4.3 Data Checking at the Beginning of the Imaging Session2.5 Patient Position and Image Acquisition2.5.1 Static or Dynamic Thermal Imaging2.6 Thermal Image AnalysisReferences

Chapter 3 Basic Approaches of Artificial Intelligence and Machine Learning in Thermal Image Processing
3.1 Image Source3.2 Data Transfer from Camera to the Analyzing Software3.3 Pre-Processing3.3.1 Thermal Image Color Modes3.3.2 Image Enhancement3.3.3 Multispectral Images3.3.4 Image Registration3.3.5 Image Fusion3.4 Segmentation Techniques3.4.1 Clustering-Based Segmentation Algorithms3.4.2 Threshold-Based Segmentation3.4.3 Region-Based Segmentation Algorithms3.4.4 Active Contour3.4.5 Edge Detection for Image Segmentation3.4.6 Watershed Segmentation3.4.7 Deep Learning Models for Image Segmentation3.5 Artifact Suppression3.6 Feature Extraction and Classification3.6.1 GRAY Level Co-occurrence Matrix3.6.2 Speeded-Up Robust Features3.6.3 Gray Level Run Length Matrix3.6.4 Local Binary Pattern3.6.5 Gray Level Size Zone Matrix3.6.6 Local Binary Gray Level Co-occurrence Matrix3.6.7 Scale-Invariant Feature Transform3.6.8 Local Directional Pattern3.6.9 Segmentation-Based Fractal Texture Analysis3.7 Machine Learning Classifiers3.7.1 Logistic Regression3.7.2 Naïve Bayes Classifier3.7.3 K-Nearest Neighbors3.7.4 Decision Tree3.7.5 Support Vector Machine3.7.6 Random Forest3.7.7 Bagging3.7.8 Logit Boost3.7.9 K-Star3.8 Deep Learning Classifier3.8.1 Background3.8.2 Basic Architecture3.8.3 Batch Normalization3.8.4 Drop Out3.8.5 Fully Connected Layer3.8.6 Training Procedure for CNN3.8.7 Pre-Processing and Augmentation of Data3.8.8 Initialization of Parameters3.8.9 CNN Regularization3.8.10 Choosing an Optimizer3.9 Performance Metrics3.9.1 Confusion Matrix3.9.2 Accuracy3.10 Pre-Trained Network Models3.10.1 VGG Architecture3.10.2 ResNet V23.10.3 DenseNet 121 (Densely Connected Convolutional Networks)3.10.4 MobileNet3.11 ConclusionReferences
Chapter 4 Thermal Imaging for Arthritis Evaluation in a Small Animal Model
4.1 Induction and Evaluation of Arthritis4.1.1 Collagen-Induced Arthritis4.1.2 Mono-arthritis Model4.1.3 Pristane-Induced Arthritis4.1.4 Adjuvant-Induced Arthritis4.1.5 Antigen-Induced Arthritis4.1.6 Proteoglycan (PG)-Induced Arthritis4.1.7 Carrageenin-Induced Arthritis4.2 Thermal Image Acquisition4.3 Thermal Image Analysis4.4 Histopathology Evaluation4.4.1 Histopathological Analysis of Synovial Fluid4.5 ConclusionReferences
Chapter 5 Thermal Imaging for Inflammatory Arthritis Evaluation
5.1 Patients and Methods5.2 Disease Activity Assessment5.3 Temperature Indices5.3.1 Heat Distribution Index5.4 Thermal Image Acquisition Protocol5.5 Thermal Image Segmentation5.5.1 Threshold Method5.5.2 Edge Detection5.5.3 k-Means Clustering5.5.4 Watershed Segmentation5.6 Statistical Feature Extraction and Classification5.6.1 Oriented Fast and Rotated BRIEF5.6.2 Support Vector Machine5.6.3 k-Nearest Neighbor5.6.4 Linear Discriminant Analysis (LDA)5.6.5 Random Forest Classifiers5.6.6 Naïve Bayes Classifier5.7 Findings and Conclusion5.8 Case Study5.8.1 Color Doppler Ultrasonographic Evaluation5.8.2 Case Reports5.8.3 Results of CDUS Examination5.8.4 Comparison of Clinical, Thermographic, and CDUS Findings5.8.5 Discussion5.8.6 Limitations and RecommendationsReferences
Chapter 6 Potential of Thermal Imaging to Detect Complications in Diabetes: Rationale for Diabetes Screening with Thermal Imaging
6.1 Introduction6.1.1 Diagnosis6.1.2 Thermography in Diabetes Evaluation6.2 Selection of Study Population6.2.1 Subject Preparation6.3 Measurement Protocol6.4 Study of Thermograms in Subjects with Diabetes6.4.1 Facial Thermal Imaging Measurements6.4.2 Thermal Imaging in Diabetic Foot6.5 Computer-Aided System for Screening of Diabetic Complications6.5.1 Features6.5.2 Automated Deep Learning Classifier6.6 ConclusionReferences
Chapter 7 Thermal Imaging in Detection of Fever for Infectious Diseases
7.1 Human Body Temperature7.1.1 Fever Development Due to Infectious Diseases7.1.2 Measurement Protocol7.2 Fever Detection Systems7.2.1 Conventional Methods7.2.2 AI-Based Fever Screening Systems7.3 ConclusionAcknowledgmentReferences
Chapter 8 Ethical Aspects in Thermal Imaging Research
8.1 Introduction8.2 General Ethical Principles8.3 Guidelines for Good Clinical Research Practice8.4 Responsible Conduct of Research8.5 Ethical Issues Review Procedure8.6 Informed Consent Process8.7 Issues Related to Medical Datasets8.8 General Guidelines when Using Thermal Imaging for Clinical Screening8.9 Ethical Considerations in Thermal Imaging Research8.9.1 Infrared Imaging Systems as Medical Devices8.9.2 Patient Management Protocol8.9.3 Interpretation and Reporting8.10 SummaryAppendix IProforma to Be Submitted to the Institute Ethics Committee (Human Studies) (template)Appendix IIInformed Consent Form (Template)References
Index

Overview

This book provides a description of designing and developing a computer assisted diagnosis (CAD) system based on thermography for diagnosing some of the common ailments such as arthritis, diabetes, and fever. It introduces applications of machine and deep learning methods, and convolutional neural networks in the development of CAD system.

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