book

Medical Image Processing

Name: Medical Image Processing
ISBN: 9783111435992

by Satya Prakash Yadav, Abhishek Kumar, Ashutosh Kumar Dubey, Victor C. de Albuquerque, Adrian Rodriguez-Aguiñaga, Angeles Quezada

September 2024

Intermediate to advanced

397 pages

14h 50m

English

De Gruyter

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List of Authors
Shanthana S., Bhavani M., Sangeetha D. 1 Medical Image Processing: A Multimodal Fusion Technique
1.1 Introduction to Medical Image Fusion Process1.1.1 Overview of Medical Imaging Modalities1.1.2 Importance of Image Fusion in Medical Imaging1.1.3 Historical Development of Image Fusion Techniques1.1.4 Scope and Objectives1.1.5 Structure of the Chapter1.2 Fundamentals of Medical Image Processing1.2.1 Basics of Digital Image Processing1.2.2 Preprocessing Techniques for Medical Images1.2.3 Segmentation Methods in Medical Imaging1.2.4 Feature Extraction and Selection1.3 Multimodal Medical Imaging Techniques1.3.1 Magnetic Resonance Imaging (MRI)1.3.2 Computed Tomography (CT)1.3.3 Positron Emission Tomography (PET)1.3.4 Single-Photon Emission Computed Tomography (SPECT)1.3.5 Ultrasound Imaging1.3.6 X-Ray Imaging1.3.7 Nuclear Imaging1.4 Principles of Image Fusion1.4.1 Introduction to Image Fusion1.4.2 Image Registration Techniques1.4.3 Fusion Techniques and Algorithms1.4.4 Performance Metrics for Image Fusion Evaluation1.5 Multimodal Fusion Techniques1.5.1 Multiresolution Fusion Methods1.5.2 Transform-Based Fusion Approaches1.5.3 Statistical Fusion Models1.5.4 Machine Learning Techniques for Fusion1.5.5 Deep Learning Architectures for Fusion1.6 Applications of Medical Image Fusion1.6.1 Image-Guided Surgery1.6.2 Disease Diagnosis and Monitoring1.6.3 Treatment Planning and Assessment1.6.4 Neuroimaging Applications1.6.5 Oncology Imaging1.6.6 Cardiovascular Imaging
K. Senthilvadivu, G. Mohan 2 Image Fusion Mathematics Theory and Practice
2.1 Mathematical Foundations of Image Fusion2.1.1 Linear Algebra Basics2.1.2 Signal Representation and Transformations2.1.3 Probability and Statistics in Image Fusion2.2 Image Fusion Techniques2.2.1 Pixel-Level Fusion Methods2.2.2 Feature-Level Fusion Methods2.2.3 Decision-Level Fusion Methods2.3 Mathematical Models for Image Fusion2.3.1 Multiscale Fusion Models2.3.2 Statistical Fusion Models2.4 Performance Evaluation Metrics2.4.1 Objective Metrics2.4.2 Subjective Metrics2.5 Implementation Considerations2.5.1 Computational Complexity Analysis2.5.2 Hardware Acceleration Techniques2.5.3 Software Tools and Libraries for Image Fusion2.6 Case Studies and Applications2.6.1 Remote Sensing Image Fusion2.6.2 Medical Image Fusion Applications2.6.3 Surveillance and Security Applications
G. Venkataramana Sagar, Sunil Kumar Bandili, Durgaprasad Navulla 3 Current Trends in High-Resolution Image Reconstruction
3.1 Introduction to High-Resolution Image Reconstruction3.1.1 Overview of High-Resolution Imaging3.1.2 Importance of High Resolution in Medical Imaging3.1.3 Historical Development of High-Resolution Techniques3.1.4 Scope and Objectives of the Chapter3.2 Fundamentals of High-Resolution Imaging3.2.1 Basic Concepts and Definitions3.2.2 Types of High-Resolution Techniques3.2.3 Challenges in High-Resolution Reconstruction3.3 Traditional Approaches to High-Resolution Reconstruction3.3.1 Interpolation-Based Methods3.3.2 Multiframe High-Resolution Techniques3.3.3 Single-Frame High-Resolution Algorithms3.4 Advanced High-Resolution Techniques3.4.1 Deep Learning-Based Approaches3.4.2 Generative Adversarial Networks (GANs) for Super-Resolution3.4.3 Convolutional Neural Networks (CNNs) for High Resolution3.4.4 Sparse Representation-Based High Resolution3.5 Applications of High Resolution in Medical Imaging3.5.1 Enhancing Resolution in MRI Images3.5.2 Improving Spatial Resolution in CT Scans3.5.3 High Resolution in Ultrasound Imaging3.5.4 Super-Resolved PET and SPECT Imaging
Murali Kalipindi, M. Madiajagan, Balasubramani S. 4 Image Fusion Through Multiresolution Oversampled Decompositions
4.1 Introduction to Multiresolution Oversampled Decompositions4.1.1 Overview of Multiresolution Analysis4.1.2 Importance of Oversampled Decompositions in Image Fusion4.1.3 Historical Development and Evolution of Multiresolution Techniques4.1.4 Scope and Objectives of the Chapter4.2 Fundamentals of Multiresolution Analysis4.2.1 Basics of Wavelet Transform4.2.2 Pyramid Decomposition Techniques4.2.3 Introduction to Oversampling in Multiresolution Analysis4.3 Techniques for Image Fusion Through Multiresolution Oversampled Decompositions4.3.1 Wavelet-Based Fusion Methods4.3.2 Curvelet Transform Fusion Approaches4.3.3 Contourlet Transform Fusion Techniques4.3.4 Shearlet Transform Fusion Strategies4.4 Performance Evaluation of Fusion Through Multiresolution Oversampled Decompositions4.4.1 Objective Metrics for Fusion Quality Assessment4.4.2 Subjective Evaluation Criteria4.4.3 Comparative Analysis with Other Fusion Techniques4.5 Applications of Multiresolution Oversampled Decomposition Fusion4.5.1 Medical Image Fusion Applications4.5.2 Remote Sensing and Geographic Information Systems4.5.3 Surveillance and Security Imaging4.5.4 Multimedia Fusion Applications
K. Prasuna, Vidyabharathi Dakshinamurthi, D. Vishnu Sakthi 5 Mathematical Models for Remote Sensing Image Processing
5.1 Introduction to Remote Sensing Image Processing5.1.1 Overview of Remote Sensing and Its Applications5.1.2 Importance of Mathematical Models in Remote Sensing Image Processing5.1.3 Historical Context and Evolution of Mathematical Models in Remote Sensing5.1.4 Scope and Objectives of the Chapter5.2 Fundamentals of Remote Sensing Image Acquisition5.2.1 Principles of Remote Sensing Systems5.2.2 Types of Remote Sensing Sensors and Platforms5.2.3 Image Acquisition Techniques and Parameters5.3 Mathematical Models for Image Enhancement in Remote Sensing5.3.1 Histogram Equalization and Stretching Techniques5.3.2 Spatial Filtering Models5.3.3 Frequency Domain Filtering Approaches5.4 Mathematical Models for Image Classification and Segmentation5.4.1 Supervised and Unsupervised Classification Models5.4.2 Clustering Algorithms for Image Segmentation5.4.3 Machine Learning Models for Remote Sensing Image Classification5.5 Mathematical Models for Image Registration and Geometric Correction5.5.1 Transformation Models for Image Registration5.5.2 Geometric Correction Techniques5.5.3 Dereferencing and Orthorectification Methods5.6 Applications of Mathematical Models in Remote Sensing5.6.1 Land Cover and Land Use Classification5.6.2 Environmental Monitoring and Change Detection5.6.3 Urban Planning and Management5.6.4 Disaster Management and Response
N. Satheesh Kumar, S. Shanthi 6 Component Analysis and Medical Image Fusion
6.1 Introduction to Component Analysis in Medical Image Fusion6.1.1 Overview of Component Analysis Techniques6.1.2 Importance of Component Analysis in Medical Imaging6.1.3 Scope and Objectives of the Chapter6.2 Fundamentals of Component Analysis6.2.1 Principal Component Analysis (PCA)6.2.2 Independent Component Analysis (ICA)6.2.3 Nonnegative Matrix Factorization (NMF)6.2.4 Sparse Coding and Dictionary Learning6.3 Component Analysis-Based Fusion Techniques6.3.1 PCA-Based Fusion Approaches6.3.2 ICA-Based Fusion Methods6.3.3 NMF-Based Fusion Strategies6.3.4 Sparse Coding-Based Fusion Algorithms6.4 Performance Analysis of Component Analysis-based Fusion6.4.1 Objective Metrics for Fusion Quality Assessment6.4.2 Subjective Evaluation Criteria6.4.3 Comparative Analysis with Other Fusion Techniques6.5 Applications of Component Analysis in Medical Image Fusion6.5.1 Fusion of MRI and CT Images6.5.2 Multimodal Fusion in Neuroimaging6.5.3 Fusion for Disease Diagnosis and Treatment Planning6.5.4 Integration with Image-Guided Surgery
K. Lakshmi Narayanan, M. Radha, U. Lenin Marksia 7 Soft Computing Approaches to Medical Image Fusion
7.1 Introduction to Soft Computing in Medical Image Fusion7.1.1 Overview of Soft Computing Techniques7.1.2 Importance of Soft Computing in Medical Imaging7.1.3 Scope and Objectives of the Chapter7.2 Fundamentals of Soft Computing Techniques7.2.1 Fuzzy Logic Systems7.2.2 Neural Networks7.2.3 Genetic Algorithms7.2.4 Swarm Intelligence7.3 Soft Computing-Based Fusion Techniques7.3.1 Fuzzy Logic-Based Fusion Methods7.3.2 Neural Network-Based Fusion Approaches7.3.3 Genetic Algorithm-Based Fusion Strategies7.3.4 Swarm Intelligence-Based Fusion Algorithms7.4 Performance Analysis of Soft Computing-Based Fusion7.4.1 Objective Metrics for Fusion Quality Assessment7.4.2 Subjective Evaluation Criteria7.4.3 Comparative Analysis with Other Fusion Techniques7.5 Applications of Soft Computing in Medical Image Fusion7.5.1 Fusion in Radiology and Diagnostic Imaging7.5.2 Multimodal Fusion for Disease Diagnosis7.5.3 Integration with Image-Guided Interventions7.5.4 Soft Computing Approaches for Therapeutic Planning
M. Sheerin Banu, M. Madiajagan, Muralidharan J. 8 Mathematical Techniques in Multispectral Image Fusion
8.1 Introduction to Multispectral Image Fusion8.1.1 Overview of Multispectral Imaging8.1.2 Importance of Fusion in Multispectral Imaging8.1.3 Scope and Objectives of the Chapter8.2 Fundamentals of Multispectral Imaging8.2.1 Principles of Multispectral Data Acquisition8.2.2 Characteristics of Multispectral Images8.2.3 Challenges in Multispectral Image Processing8.3 Mathematical Models for Multispectral Image Fusion8.3.1 Spectral Transformation Techniques8.3.2 Spatial-Spectral Fusion Methods8.3.3 Multiscale Fusion Approaches8.4 Techniques for Fusion of Multispectral Images8.4.1 Principal Component Analysis (PCA)-Based Fusion8.4.2 Wavelet Transform-Based Fusion8.4.3 Deep Learning Approaches for Multispectral Fusion8.5 Performance Evaluation of Multispectral Image Fusion8.5.1 Objective Metrics for Fusion Quality Assessment8.5.2 Subjective Evaluation Criteria8.5.3 Comparative Analysis with Other Fusion Techniques8.6 Applications of Multispectral Image Fusion8.6.1 Agriculture and Environmental Monitoring8.6.2 Remote Sensing and Geographic Information Systems8.6.3 Defense and Security Applications8.6.4 Medical Imaging and Healthcare
V. P. Sriram, Annapantula Sudhakar, K. Prabhu Chandran 9 Fusion of Artificial Intelligence and Machine Learning for Advanced Image Processing
9.1 Introduction to Fusion of AI and Machine Learning in Image Processing9.1.1 Overview of Artificial Intelligence and Machine Learning9.1.2 Importance of Fusion in Image Processing9.1.3 Scope and Objectives of the Chapter9.2 Fundamentals of Artificial Intelligence and Machine Learning Techniques9.2.1 Basics of Artificial Neural Networks (ANNs)9.2.2 Supervised, Unsupervised, and Reinforcement Learning9.2.3 Deep Learning Architectures: CNNs, RNNs, and GANs9.2.4 Transfer Learning and Pretrained Models9.3 Fusion Techniques Using AI and Machine Learning9.3.1 Deep Learning-Based Image Fusion Approaches9.3.2 Transfer Learning for Image Fusion9.3.3 Reinforcement Learning for Adaptive Fusion Strategies9.3.4 Meta-Learning Approaches for Fusion Optimization9.4 Performance Evaluation of AI and ML-Based Fusion9.4.1 Objective Metrics for Fusion Quality Assessment9.4.2 Subjective Evaluation Criteria9.4.3 Comparative Analysis with Traditional Fusion Techniques9.5 Applications of AI and Machine Learning in Advanced Image Processing9.5.1 Medical Imaging Diagnosis and Prognosis9.5.2 Remote Sensing and Earth Observation9.5.3 Autonomous Vehicles and Robotics9.5.4 Security and Surveillance Systems

N. Banupriya, N. Padmavathi, P. Megala 10 Fusion of Multispectral and Panchromatic Images as an Optimization Problem
10.1 Introduction to Multispectral and Panchromatic Image Fusion10.1.1 Overview of Multispectral and Panchromatic Imaging10.1.2 Importance of Fusion in Remote Sensing Applications10.1.3 Scope and Objectives of the Chapter10.2 Fundamentals of Multispectral and Panchromatic Imaging10.2.1 Characteristics of Multispectral and Panchromatic Images10.2.2 Differences in Spatial and Spectral Resolutions10.2.3 Challenges in Fusion of Multispectral and Panchromatic Data10.3 Optimization Techniques for Fusion10.3.1 Objective Functions for Fusion Optimization10.3.2 Optimization Algorithms for Fusion10.3.3 Constraints and Regularization in Fusion Optimization10.4 Fusion Models as Optimization Problems10.4.1 Sparse Representation-Based Fusion Models10.4.2 Convolutional Neural Network Models10.4.3 Variational Methods for Fusion10.5 Performance Evaluation of Fusion Optimization Techniques10.5.1 Objective Metrics for Fusion Quality Assessment10.5.2 Subjective Evaluation Criteria10.5.3 Comparative Analysis with Traditional Fusion Techniques10.6 Applications of Multispectral and Panchromatic Image Fusion10.6.1 Agriculture and Crop Monitoring10.6.2 Environmental Monitoring and Land Cover Classification10.6.3 Urban Planning and Infrastructure Development10.6.4 Disaster Management and Response
Mary Shiba C., Puvvada Nagesh, Ramesh S. 11 Image Fusion Using Optimization of Statistical Measurements
11.1 Introduction to Medical Image Processing11.1.1 Overview of Medical Imaging Modalities11.1.2 Importance of Image Fusion in Medical Imaging11.2 Fundamentals of Image Fusion11.2.1 Image Fusion Techniques Overview11.2.2 Taxonomy of Image Fusion Methods11.3 Multimodal Medical Image Fusion11.3.1 Fusion of MRI and CT Images11.3.2 Fusion of PET and CT Images11.3.3 Fusion of Ultrasound and MRI Images11.4 Optimization Techniques in Image Fusion11.4.1 Genetic Algorithms for Image Fusion11.4.2 Swarm Intelligence Approaches for Image Fusion11.4.3 Optimization of Statistical Measurements11.5 Image Fusion Using Optimization of Statistical Measurements11.5.1 Introduction to Statistical Measurements11.5.2 Optimization Methods for Statistical Image Fusion11.5.3 Case Studies and Applications11.6 Evaluation Metrics for Image Fusion11.6.1 Objective Metrics11.6.2 Subjective Evaluation Methods11.7 Challenges and Future Directions11.7.1 Current Challenges in Medical Image Fusion11.7.2 Future Trends and Emerging Technologies11.8 Conclusion
S. Karthik, M. A. Shanthi, R. Sabitha, B. Anuradha 12 Empirical Mode Decomposition for Simultaneous Image Enhancement and Fusion
12.1 Introduction to Empirical Mode Decomposition (EMD)12.2 EMD-Based Image Enhancement Techniques12.3 Simultaneous Image Fusion Using EMD12.4 Case Studies and Applications12.5 Future Directions
M. S. Kavitha, Suriya A., S. Karthik, B. Chellaprabha 13 Multimodality Sensor Image Fusion
13.1 Introduction to Multimodality Sensor Image Fusion13.1.1 Overview of Sensor Fusion13.1.2 Importance of Multimodality in Image Fusion13.2 Fundamentals of Sensor Fusion13.2.1 Single Modality Image Processing13.2.2 Multimodality Image Processing13.2.3 Fusion Techniques Overview13.3 Multimodal Sensor Technologies13.3.1 Description of Different Sensor Types13.3.2 Advantages and Limitations of Each Modality13.4 Fusion Techniques for Multimodal Sensor Data13.4.1 Pixel-Level Fusion Methods13.4.2 Feature-Level Fusion Methods13.4.3 Decision-Level Fusion Methods13.5 Applications of Multimodality Sensor Image Fusion13.5.1 Medical Imaging13.5.2 Remote Sensing13.5.3 Surveillance and Security13.5.4 Robotics and Autonomous Systems13.6 Challenges and Future Directions13.6.1 Current Limitations in Multimodal Fusion13.6.2 Emerging Trends and Technologies13.6.3 Future Research Directions
R. Sabitha, Shirley Josephine Mary R., J. Shanthini, A. Christopher Paul 14 Medical Image Fusion Method by Deep Learning
14.1 Introduction to Medical Image Fusion with Deep Learning14.1.1 Overview of Medical Image Fusion14.1.2 Introduction to Deep Learning in Medical Imaging14.2 Deep Learning Techniques for Medical Image Fusion14.2.1 Convolutional Neural Networks (CNNs) for Fusion14.2.2 Generative Adversarial Networks (GANs) for Fusion14.2.3 Autoencoder-Based Fusion Methods14.3 Multimodal Medical Image Datasets14.3.1 Description of Commonly Used Datasets14.3.2 Challenges in Dataset Collection and Preprocessing14.4 Deep Learning Architectures for Medical Image Fusion14.4.1 Architectural Design Considerations14.4.2 Network Structures for Multimodal Fusion14.4.3 Training Strategies and Optimization Techniques14.5 Applications of Deep Learning-Based Medical Image Fusion14.5.1 Image-Guided Surgery14.5.2 Disease Diagnosis and Treatment Planning14.5.3 Medical Image Analysis and Segmentation14.6 Performance Evaluation and Comparison14.6.1 Metrics for Assessing Fusion Quality14.6.2 Benchmarking Deep Learning Models14.6.3 Comparative Studies with Traditional Fusion Methods14.7 Challenges and Future Directions14.7.1 Current Limitations and Open Challenges14.7.2 Potential Solutions and Future Research Directions14.7.3 Integration with Emerging Technologies in Healthcare
N. Karthikeyan, Vivekanandan V., Bharathi B., M. S. Kavitha 15 Quaternion-Based Sparse Techniques for Multimodal and Multispectral Image Processing
15.1 Introduction to Quaternion-Based Sparse Techniques15.1.1 Overview of Multimodal and Multispectral Image Processing15.1.2 Introduction to Quaternion Algebra15.2 Sparse Representation and Its Role in Multimodal Image Processing15.2.1 Sparse Coding and Dictionary Learning15.2.2 Applications of Sparse Techniques in Image Fusion15.3 Quaternion-Based Representation of Multimodal Data15.3.1 Basics of Quaternion Algebra15.3.2 Quaternion Sparse Representation Model15.4 Sparse Fusion Techniques for Multimodal, Multispectral Images15.4.1 Quaternion Sparse Representation for Fusion15.4.2 Sparse Decomposition Methods in Quaternion Domain15.5 Applications of Quaternion-Based Sparse Techniques15.5.1 Medical Imaging Fusion15.5.2 Remote Sensing Data Fusion15.5.3 Multispectral Image Enhancement15.6 Performance Evaluation and Comparative Studies15.6.1 Evaluation Metrics for Multimodal Image Fusion15.6.2 Comparative Analysis with Traditional Fusion Technique15.7 Challenges and Future Directions15.7.1 Limitations of Quaternion-Based Sparse Techniques15.7.2 Future Research Directions in Quaternion Image Fusion15.7.3 Integration with Advanced Technologies for Improved Fusion Results
V. V. Karthikeyan, Prashanthini K., P. Sumathi, S. Karthik 16 Quaternion Neural Networks for Geometrical Operators in High-Dimensional Quaternion Space
16.1 Introduction to Quaternion Neural Networks (QNNs)16.1.1 Overview of Neural Networks in Image Processing16.1.2 Introduction to Quaternion Algebra and Its Applications in Deep Learning16.2 Basics of Quaternion Representation and Operations16.2.1 Quaternion Algebra Fundamentals16.2.2 Quaternion Convolutions and Pooling Operations16.3 Quaternion Neural Network Architecture16.3.1 Design Considerations for QNNs16.3.2 Layers and Activation Functions in Quaternion Space16.4 Geometrical Operators in High-Dimensional Quaternion Space16.4.1 Rotation, Translation, and Scaling Operations16.4.2 Quaternion-Based Transformations in Image Processing16.5 Applications of Quaternion Neural Networks16.5.1 Image Classification and Recognition16.5.2 Object Detection and Tracking16.5.3 3D Object Reconstruction16.6 Training and Optimization Methods for QNNs16.6.1 Backpropagation in Quaternion Space16.6.2 Regularization and Optimization Methods16.7 Performance Evaluation and Comparative Analysis16.7.1 Metrics for Evaluating QNN Performance16.7.2 Comparative Studies with Traditional Neural Networks16.8 Challenges and Future Directions16.8.1 Current Limitations of QNNs16.8.2 Potential Applications and Future Research Directions16.8.3 Integration with Emerging Technologies for Improved Results
N. Banupriya, G. Nirmala, N. Vijayaraghavan 17 Image Dehazing Using Quaternion Complex Algebra-Based Neural Networks
17.1 Introduction to Image Dehazing17.1.1 Overview of Haze and Atmospheric Effects in Images17.1.2 Importance of Image Dehazing in Computer Vision17.2 Challenges in Image Dehazing17.2.1 Physical Models of Haze Formation17.2.2 Limitations of Traditional Dehazing Method17.3 Introduction to Quaternion Complex Algebra17.3.1 Basics of Quaternion and Complex Numbers17.3.2 Quaternion Complex Algebra and Its Applications in Image Processing17.4 Quaternion Complex Algebra-Based Neural Networks for Dehazing17.4.1 Architecture Design Considerations17.4.2 Quaternion Complex Convolution Layers17.4.3 Activation Functions and Regularization Techniques17.5 Dehazing Techniques Using Quaternion Complex Algebra17.5.1 Modeling Haze Removal as an Optimization Problem17.5.2 Learning-Based Approaches for Haze Removal17.6 Applications of Quaternion Complex Algebra-Based Dehazing Networks17.6.1 Outdoor Scene Enhancement17.6.2 Surveillance and Remote Sensing Applications17.6.3 Automotive Vision Systems17.7 Training Strategies and Optimization Methods17.7.1 Data Augmentation and Preprocessing Techniques17.7.2 Training Procedures for Quaternion Complex Networks17.8 Performance Evaluation and Comparative Analysis17.8.1 Evaluation Metrics for Dehazing Quality17.8.2 Comparative Studies with Traditional Dehazing Methods17.9 Challenges and Future Directions17.9.1 Remaining Challenges in Quaternion Complex Algebra-Based Dehazing17.9.2 Potential Research Directions for Improving Dehazing Performance17.9.3 Integration with Advanced Imaging Technologies for Enhanced Results
M. Sunil Kumar, T. Gnanasekaran, Balambigai Subramanian 18 Deep Learning Model for Image Fusion and Accurate Classification of Remote Sensing Images
18.1 Introduction18.1.1 Background18.1.2 Motivation18.1.3 Objectives18.2 Literature Review18.2.1 Image Fusion Techniques18.2.2 Remote Sensing Image Classification18.2.3 Deep Learning in Remote Sensing18.3 Methodology18.3.1 Data Collection18.3.2 Preprocessing18.3.3 Image Fusion18.3.4 Deep Learning Model Architecture18.3.5 Training Process18.4 Image Fusion Techniques18.4.1 Overview18.4.2 Spatial Domain Methods18.4.3 Transform Domain Methods18.4.4 Hybrid Methods18.5 Remote Sensing Image Classification18.5.1 Importance and Challenges18.5.2 Traditional Classification Techniques18.5.3 Deep Learning for Classification18.6 Deep Learning Model Design18.6.1 Architecture Overview18.6.2 Feature Extraction Layers18.6.3 Fusion Mechanisms18.6.4 Classification Layers18.7 Experimental Setup18.7.1 Dataset Description18.7.2 Evaluation Metrics18.7.3 Implementation Details18.8 Results and Discussion18.8.1 Performance Evaluation18.8.2 Comparison with Baseline Methods18.8.3 Analysis of Fusion Techniques18.8.4 Impact of Deep Learning Model18.9 Case Studies18.9.1 Real-World Applications18.9.2 Performance in Different Scenarios18.10 Discussion18.10.1 Insights from Results18.10.2 Limitations and Challenges18.10.3 Future Directions18.11 Conclusion18.11.1 Summary of Findings18.11.2 Contributions18.11.3 Implications and Future Work
B. Aruna Devi, A. Srilakshmi, M. Jayaprakash 19 Multimodal Medical Supervised Image Fusion Method
19.1 Introduction19.1.1 Background of Medical Imaging Modalities19.1.2 Importance of Image Fusion in Medical Imaging19.1.3 Objectives of the Study19.2 Literature Review19.2.1 Multimodal Medical Imaging Techniques19.2.2 Image Fusion Methods in Medical Imaging19.2.3 Challenges in Multimodal Image Fusion19.3 Methodology Overview19.3.1 Data Acquisition and Preprocessing19.3.2 Supervised Image Fusion Framework19.3.3 Evaluation Metrics19.4 Medical Imaging Modalities19.4.1 Description and Characteristics19.4.2 Complementary Information in Different Modalities19.5 Image Fusion Techniques19.5.1 Traditional Methods19.5.2 Deep Learning Approaches19.5.3 Supervised Fusion Techniques19.6 Supervised Image Fusion Framework19.6.1 Conceptual Framework19.6.2 Feature Extraction and Selection19.6.3 Fusion Strategy19.6.4 Learning Mechanisms19.7 Experimental Setup19.7.1 Dataset Description19.7.2 Implementation Details19.7.3 Evaluation Protocol19.8 Results and Discussion19.8.1 Performance Evaluation Metrics19.8.2 Comparative Analysis with Existing Methods19.8.3 Discussion on Fusion Strategies and Results
R. Priyanka Pramila, S. Sujatha, Subbulakshmi R. 20 Medical Image Fusion Using Deep Learning Mechanisms
20.1 Introduction20.1.1 Background of Medical Image Fusion20.1.2 Importance of Deep Learning in Medical Imaging20.1.3 Objectives of the Chapter20.2 Literature Review20.2.1 Medical Image Fusion Techniques20.2.2 Deep Learning Applications in Medical Imaging20.2.3 Challenges and Opportunities in Medical Image Fusion with Deep Learning20.3 Deep Learning Architectures for Image Fusion20.3.1 Overview of Deep Learning Models20.3.2 CNN-Based Fusion Architectures20.3.3 Generative Adversarial Networks (GANs) for Fusion20.3.4 Autoencoder-Based Fusion Approaches20.4 Dataset and Preprocessing20.4.1 Description of Medical Image Datasets20.4.2 Data Augmentation Techniques20.4.3 Preprocessing Steps for Deep Learning20.5 Proposed Deep Learning Fusion Model20.5.1 Architecture Design20.5.2 Feature Extraction Layers20.5.3 Fusion Mechanisms20.5.4 Loss Functions and Optimization20.6 Training and Fine-Tuning Process20.6.1 Training Strategy20.6.2 Hyperparameter Tuning20.6.3 Transfer Learning Approaches20.7 Evaluation Metrics and Experimental Setup20.7.1 Performance Metrics for Fusion Quality20.7.2 Comparative Evaluation with Existing Methods20.7.3 Experimental Settings and Implementation Details20.8 Results and Discussion20.8.1 Quantitative Evaluation of Fusion Quality20.8.2 Visual Analysis of Fused Images20.8.3 Discussion on Performance and Interpretation20.9 Case Studies and Applications20.9.1 Clinical Applications of Fused Images20.9.2 Real-World Scenarios and Use Cases20.10 Challenges and Future Directions20.10.1 Limitations of Current Approaches20.10.2 Future Research Directions in Medical Image Fusion20.10.3 Integration with Clinical Workflow20.11 Conclusion
M. Sunil Kumar, M. G. Meedphin Arasi, S. Meenakshi 21 Multifocus Image Fusion Using Content-Adaptive Blurring
21.1 Introduction to Medical Image Processing21.2 Fundamentals of Image Fusion21.3 Multimodal Medical Image Fusion Techniques21.4 Challenges in Medical Image Fusion21.5 Importance of Multifocus Image Fusion21.6 Overview of Content-Adaptive Blurring21.7 Traditional Methods in Multifocus Image Fusion21.8 Content-Adaptive Blurring Techniques21.9 Applications of Multifocus Image Fusion in Medicine21.10 Evaluation Metrics for Image Fusion Algorithms21.11 Case Studies in Multifocus Image Fusion21.12 Future Directions in Medical Image Fusion21.13 Conclusion and Summary
N. Karthikeyan, Ragavi Priya S., S. Karthik, B. Anuradha 22 Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function
22.1 Introduction to Content-Based Image Retrieval (CBIR)22.2 Importance of Top Precision Performance Measure22.3 Challenges in CBIR22.4 Overview of Learning Similarity Function22.5 Traditional Approaches to CBIR22.6 Optimization Techniques for Top Precision Performance Measure22.7 Learning Similarity Function Models22.8 Experimental Evaluation Methods22.9 Case Studies on Optimizing Top Precision Performance Measure22.10 Future Directions in CBIR Optimization22.11 Conclusion and Summary
Arulmozhi A. K., Chairma Lakshmi K. R., G. Nirmala, M. Dinesh Babu 23 Performance Evaluation of Image Fusion Techniques
23.1 Introduction to Image Fusion Techniques23.2 Importance of Performance Evaluation23.3 Challenges in Evaluating Image Fusion Techniques23.4 Overview of Performance Metrics23.5 Traditional Approaches to Performance Evaluation23.6 Objective Metrics for Image Fusion23.7 Subjective Metrics for Image Fusion23.8 Comparative Evaluation Methods23.9 Experimental Design and Data Collection23.10 Case Studies on Performance Evaluation23.11 Future Directions in Image Fusion Evaluation23.12 Conclusion and Summary
Index

Content preview from Medical Image Processing

22 Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function

N. Karthikeyan

Ragavi Priya S.

S. Karthik

B. Anuradha

Abstract

Content-based image retrieval (CBIR) is an extensively used technique for finding relevant pixels in massive databases based totally on their visible content. One of the key performance measures for comparing the effectiveness of CBIR structures is pinnacle precision, which measures the proportion of relevant pictures of the various pinnacle retrieved outcomes. But, achieving high pinnacle precision in CBIR is a tough task due to the excessive dimensionality of image facts and the subjective nature of relevance judgments. In this paper, we recommend a method for ...

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Medical Image Reconstruction, 2nd Edition

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Medical Image Processing

by Satya Prakash Yadav, Abhishek Kumar, Ashutosh Kumar Dubey, Victor C. de Albuquerque, Adrian Rodriguez-Aguiñaga, Angeles Quezada