Artificial intelligence and machine learning in 2D/3D medical image processing / edited by Rohit Raja [and three others].

Format:

Book

Contributor:

Raja, Rohit, editor.

Language:

English

Subjects (All):

Diagnostic imaging--Data processing.

Diagnostic imaging.

Imaging systems in medicine.

Physical Description:

1 online resource (215 pages)

Edition:

1st ed.

Place of Publication:

Boca Raton, Florida ; London, England ; New York : CRC Press, [2021]

Summary:

"Medical image fusion is a process which merges information from multiple images of the same scene. The fused image provides appended information that can be utilized for more precise localization of abnormalities. The use of medical image processing databases will help to create and develop more accurate and diagnostic tools"-- Provided by publisher.

Contents:

Intro

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

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

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

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

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

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

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

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

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

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

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

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

Index.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

0-429-35452-5

1-000-33707-3

9780429354526

OCLC:

1195819609