Biomedical Image Analysis: Special Applications in MRIs and CT scans

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