Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing

✍ Scribed by Rohit Raja (editor), Sandeep Kumar (editor), Shilpa Rani (editor), K. Ramya Laxmi (editor)

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 215
• Edition: 1
• Category: Library

✦ Synopsis

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management.

Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology.

This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems.

This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning.

FEATURES

• Highlights the framework of robust and novel methods for medical image processing techniques
• Discusses implementation strategies and future research directions for the design and application requirements of medical imaging
• Examines real-time application needs
• Explores existing and emerging image challenges and opportunities in the medical field

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Introduction
Editors
Contributors
1. An Introduction to Medical Image Analysis in 3D
1.1. Introduction
1.2. Comparison Between 2D and 3D Techniques in Medical Imaging
1.3. Importance of 3D Medical Image
1.4. Medical Imaging Types and Modalities
1.5. Computer Vision System Works in 3D Image Analysis
1.6. Various Techniques in 3D Image Processing in Medical Imaging
1.7. Types of Medical Imaging Compressed by 3D Medical Visualization
1.8. 3D Ultrasound Shortens The Imaging Development
1.9. Conclusion
References
2. Automated Epilepsy Seizure Detection from EEG Signals Using Deep CNN Model
2.1. Introduction
2.2. Materials and Methodology
2.2.1. Dataset
2.2.2. Normalization
2.2.3. Convolution Neural Network (CNN)
2.3. Result and Discussions
2.3.1. Experiment 1: 10-Fold Cross Validation on 90:10 Ratio
2.3.2. Experiment 2: Training and Testing Ratio Variation
2.4. Conclusion
References
3. Medical Image De-Noising Using Combined Bayes Shrink and Total Variation Techniques
3.1. Introduction
3.2. Literature Review
3.3. Theoretical Analysis
3.3.1. Median Modified Wiener Filter
3.3.2. Wavelet Transform
3.3.3. Dual Tree Complex Wavelet Transform
3.3.4. Sure Shrink
3.3.5. Bayes Shrink
3.3.6. Neigh Shrink
3.3.7. DTCWT Based De-Noising Using Adaptive Thresholding
3.4. Total Variation Technique
3.5. Pixel Level DTCWT Image Fusion Technique
3.6. Performance Evaluation Parameters
3.6.1. Peak Signal to Noise Ratio
3.6.2. Structural Similarity Index Matrix
3.7. Methodology
3.8. Results And Discussion
3.9. Conclusions And Future Scope
References
4. Detection of Nodule and Lung Segmentation Using Local Gabor XOR Pattern in CT Images
4.1. Introduction
4.2. Histories
4.3. Concepts
4.4. Causes for Lung Cancer
4.4.1. Smoking
4.4.2. Familial Predisposition
4.4.3. Lung Diseases
4.4.4. Prior Tale Containing Stroke Cancer
4.4.5. Air Pollution
4.4.6. Exposure as Far as Engine Exhaust
4.4.7. Types Containing Tumor
4.4.8. Signs and Symptoms of Lung Cancer
4.5. Solution Methodology With Mathematical Formulations
4.5.1. Feature Extraction
4.5.2. Modified Area Starting to Be Algorithm
4.5.3. Gridding
4.5.4. Selection of Seed Point
4.6. Morphological Operation
4.7. Conclusions and Future Work
References
5. Medical Image Fusion Using Adaptive Neuro Fuzzy Inference System
5.1. Introduction
5.1.1. Overview
5.1.1.1. Digital Image
5.1.1.2. Types of Digital Images
5.1.1.2.1. Binary Images
5.1.1.2.2. Grayscale Image
5.1.1.2.3. Color Image
5.1.1.3. Medical Imaging Type
5.1.1.3.1. CT Images
5.1.1.3.2. MRI Image
5.1.1.4. Image Fusion
5.1.1.4.1. Some Meanings of Fusion
5.1.1.4.2. Applications of Image Fusion
5.1.1.4.3. Medical Image Fusion
5.1.2. Literature Survey
5.1.2.1. A Brief History about Literature Survey
5.1.3. Solution Methodology
5.1.3.1. Fuzzy Logic
5.1.3.2. Fuzzy Set
5.1.3.3. Membership Functions
5.1.3.4. Fuzzy Inference System
5.1.4. Proposed Methodology
5.1.4.1. Applying to ANFIS
5.1.4.1.1. ANFIS Rule
5.1.4.1.2. RULES:
5.1.4.1.3. Merge Color Channel
5.1.5. Result and Discussion
5.1.5.1. Simulation Result
5.1.5.2. Performance Analysis
5.1.6. Conclusion and Future Scope
5.1.6.1. Future Scope
References
6. Medical Imaging in Healthcare Applications
6.1. Introduction
6.2. Image Modalities
6.2.1. PET Scan
6.2.2. Ultrasound
6.2.3. MRI Scan
6.2.4. CT Scan
6.3. Recent Trends in Healthcare Technology
6.4. Scope for Future Work
6.5. Conclusions
References
7. Classification of Diabetic Retinopathy by Applying an Ensemble of Architectures
7.1. Introduction
7.1.1. Literature Survey
7.2. Method and Data
7.2.1. Dataset Used
7.2.2. Augmentation of Dataset
7.2.3. Partition of Dataset
7.2.4. Evaluation Metrics
7.2.5. Method
7.3. Results
7.4. Conclusion
References
8. Compression of Clinical Images Using Different Wavelet Function
8.1. Introduction: Background and Need of Compression
8.2. Terminology UtilizeD for Implementation
8.3. Proposed Algorithm
8.3.1. Calculation for Picture Compression Utilizing Wavelet
8.3.1.1. Input Image
8.3.1.2. Compression Decompression and Filters
8.3.1.3. Compression
8.3.1.4. Image Reconstruction
8.3.2. Performance Analysis
8.4. Implementation and Result
8.4.1. Analysis of CT Scan Images
8.4.1.1. Wavelet Haar Function Is Used
8.5. Conclusion
References
9. PSO-Based Optimized Machine Learning Algorithms for the Prediction of Alzheimer's Disease
9.1. Introduction
9.2. Related Work
9.3. Material and Methods
9.3.1. Proposed Workflow
9.3.2. Database
9.3.3. Data Pre-processing
9.4. Particle Swarm Optimization (PSO) Techniques
9.4.1. Machine Learning Models
9.5. Experimental Results
9.6. Discussion
9.7. Conclusion
References
10. Parkinson's Disease Detection Using Voice Measurements
10.1. Introduction
10.2. Literature Survey
10.2.1. Parkinson's Syndromes
10.2.2. Symptoms
10.2.3. Causes
10.2.4. Threat Causes
10.2.5. Complications
10.3. Methodologies Used in Present Work
10.3.1. Machine Learning (ML) and Artificial Intelligence (AI)
10.3.2. Ensemble Learning
10.3.3. Advantages
10.3.4. Data Drive Machine Learning
10.3.5. Architecture
10.4. Proposed System
10.5. Testing
10.5.1. Type of Testing
10.5.2. Integration Testing
10.5.3. Functional Testing
10.6. Conclusion and Future Enhancements
References
11. Speech Impairment Using Hybrid Model of Machine Learning
11.1. Introduction
11.2. Types of Classifier
11.2.1. Naive Bayes (Classifier)
11.2.2. Support Vector Machine (SVM)
11.2.3. K-Nearest Neighbor (KNN)
11.2.4. Decision Tree
11.2.5. Random Forest
11.2.6. XGBoost
11.2.7. Extra Trees
11.3. Related Work
11.4. Proposed Work
11.5. Results and Discussions
11.6. Conclusion
References
12. Advanced Ensemble Machine Learning Model for Balanced BioAssays
12.1. Introduction
12.2. Related Work
12.3. Proposed Work
12.3.1. Ensemble Classification
12.4. Experimental Investigation
12.4.1. Dataset Report
12.4.2. Experimental Setting
12.5. Results
12.5.1. Assessment of Results
12.5.2. Assessment of the Model on the Dataset
12.6. Conclusion
References
13. Lung Segmentation and Nodule Detection in 3D Medical Images Using Convolution Neural Network
13.1. Introduction
13.2. Review of Literature
13.3. Rationale of the Study
13.3.1. Morphological Processing of the Digital Image
13.4. Objectives of Study
13.5. Proposed Methodology
13.5.1. Evaluation Results for Medical Image Handling
13.5.1.1. False Positive Rate (FPR)
13.5.1.2. False Negative Rate (FNR)
13.5.1.3. Sensitivity
13.5.1.4. Specificity
13.5.1.5. Accuracy
13.6. Expected Outcome of Research Work
13.7. Conclusion and Future work
References
Index