Biomedical Image Analysis: Special Applications in MRIs and CT scans

✍ Scribed by Pritpal Singh

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 173
• Category: Library

✦ Synopsis

This book provides an in-depth study of biomedical image analysis. It reviews and summarizes previous research work in biomedical image analysis and also provides a brief introduction to other computation techniques, such as fuzzy sets, neutrosophic sets, clustering algorithm and fast forward quantum optimization algorithm, focusing on how these techniques can be integrated into different phases of the biomedical image analysis. In particular, this book describes novel methods resulting from the fuzzy sets, neutrosophic sets, clustering algorithm and fast forward quantum optimization algorithm. It also demonstrates how a new quantum-clustering based model can be successfully applied in the context of clustering the COVID-19 CT scans. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to biomedical image analysis, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and government institutes and medical colleges.

✦ Table of Contents

Preface
Acknowledgement
Contents
1 Introduction
1.1 Related Works in Image Segmentation
1.2 Related Works in Image Clustering
1.3 Organization of the Book
References
2 Parkinson's Disease MRIs Analysis Using FuzzyClustering Approach
2.1 Introduction
2.2 Mathematical Formulation for Uncertainty Representation
2.3 The Proposed Method
2.3.1 Representation of Pixels
2.3.2 Formation of FIS
2.3.3 Measure of Uncertainty
2.3.4 Clustering of FEM
2.3.5 Pattern Visualization
2.4 Experimental Results
2.4.1 Experimental Set-Up
2.4.2 Performance Evaluation Metrics
2.4.3 Discussion on Segmentation of MRI
2.4.4 Discussion on Pattern Classification and Visualization
2.5 Conclusions and Future Directions
References
3 Parkinson's Disease MRIs Analysis Using Neutrosophic-Entropy Segmentation Approach
3.1 Introduction
3.2 Mathematical Formulation of Uncertainty
3.3 The Proposed Algorithm
3.3.1 Description of NEATSA
3.3.2 Algorithm and Computational Complexity
3.4 Experimental Results
3.4.1 Dataset Description
3.4.2 Performance Evaluation Metrics
3.4.3 Experimental Set-Up
3.4.4 Discussion on Experimental Results
3.5 Conclusions and Future Directions
References
4 Parkinson's Disease MRIs Analysis Using Neutrosophic-Entropy Clustering Approach
4.1 Introduction
4.2 Theoretical Basis
4.3 The Proposed Method
4.3.1 Description of the Proposed Method
4.4 Experimental Results
4.4.1 Dataset Description and Experimental Set-Up
4.4.2 Performance Evaluation Metrics
4.4.3 Discussion on the Results Obtained by the NEBCA
4.4.4 Discussion on the Results Obtained by the the HSV Color System
4.4.5 Discussion on the Computation Time
4.4.6 Algorithm and Computational Complexity
4.5 Conclusions and Future Directions
References
5 Brain Tumor Segmentation Using Type-2 Neutrosophic Thresholding Approach
5.1 Introduction
5.2 Motivation and Contributions
5.3 Background for the Study
5.4 The Proposed T2NS and Related Concepts
5.4.1 T2NS Theory
5.4.2 Set-Theoretic Operations and Properties for T2NS
5.4.3 Uncertainty Measurement of T2NS
5.5 The Proposed Image Segmentation Method
5.5.1 Gray Pixel Space of Input Image
5.5.2 Histogram of the GPS
5.5.3 Application of the T2NS
5.5.4 Computation of T2NSE for the T2NS
5.5.5 Determination of Thresholds
5.5.6 Segmentation of Image
5.5.7 Fusion of Segmented Images
5.6 Experimental Results
5.6.1 Dataset Description
5.6.2 Performance Evaluation Metrics
5.6.3 Visual Analysis
5.6.4 Multiple Adaptive Thresholds Selection
5.6.5 Statistical Analysis
5.6.6 Computational Complexity Analysis
5.7 Conclusions and Future Directions
References
6 COVID-19 CT Scan Image Segmentation Using Quantum-Clustering Approach
6.1 Introduction
6.2 Image Segmentation Using KMC Algorithm
6.3 The Proposed FFQOA
6.3.1 Inspiration for the FFQOA
6.3.2 Background for the FFQOA
6.3.3 Mathematical Modeling for the FFQOA
6.3.4 Personal Best and Global Best Displacements
6.3.5 The Search Scope Components
6.4 The Proposed FFQOAK Method
6.4.1 Phases of the FFQOAK Method
6.4.2 Optimization Process of the ProposedFFQOAK Method
6.5 Experimental Results
6.5.1 Dataset and Preprocessing Descriptions
6.5.2 Performance Evaluation Metrics
6.5.3 Statistical Analyses
6.5.4 Convergence Analysis
6.5.5 Visual Analysis of Segmented Images
6.6 Conclusions and Future Directions
References