Neutrosophic Set in Medical Image Analysis gives an understanding of the concepts of NS, along with knowledge on how to gather, interpret, analyze and handle medical images using NS methods. It presents the latest cutting-edge research that gives insight into neutrosophic set's novel techniques, strategies and challenges, showing how it can be used in biomedical diagnoses systems. The neutrosophic set (NS), which is a generalization of fuzzy set, offers the prospect of overcoming the restrictions of fuzzy-based approaches to medical image analysis.
- Introduces the mathematical model and concepts of neutrosophic theory and methods
- Highlights the different techniques of neutrosophic theory, focusing on applying the neutrosophic set in image analysis to support computer- aided diagnosis (CAD) systems, including approaches from soft computing and machine learning
- Shows how NS techniques can be applied to medical image denoising, segmentation and classification
- Provides challenges and future directions in neutrosophic set based medical image analysis
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1. Introduction to neutrosophy and neutrosophic environment 2. Advanced neutrosophic sets in Microscopic Image Analysis 3. Advanced neutrosophic set-based ultrasound image analysis 4. Neutrosophic set in medical image denoising 5. Advanced optimization-based neutrosophic sets for medical image denoising 6. Neutrosophic set-based denoising of optical coherence tomography images 7. A survey on neutrosophic medical image segmentation 8. Neutrosophic set in medical image clustering 9. Optimization-based neutrosophic set for medical image processing 10. Neutrosophic hough transform for blood cells nuclei detection 11. Neutrosophic sets in dermoscopic medical image segmentation 12. Neutrosophic similarity score-based entropy measure for focal and nonfocal electroencephalogram signal classification 13. Neutrosophic multiple deep convolutional neural network for skin dermoscopic image classification 14. Neutrosophic set-based deep learning in mammogram analysis 15. Challenges and future directions in neutrosophic set-based medical image analysis
Yanhui Guo is currently an Assistant professor in the Department of Computer Science at the University of Illinois at Springfield, USA. He received his B. S. degree in Automatic Control from Zhengzhou University, China, M.S. degree in Pattern Recognition and Intelligence System from Harbin Institute of Technology, China, and Ph.D. degree in the Department of Computer Science, Utah State University, USA. Dr. Guo has published more than 80 journal papers and 30 top conference papers, completed 11 grant funded research projects, and worked as an associate editor of different international journals, reviewers for top journals and conferences. His research area includes neutrosophic theory, computer vision, machine learning, big data analytics, computer aided detection/diagnosis, and computer assist surgery.
Ashour, Amira S.
Amira S. Ashour is currently an Assistant Professor and Head of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Egypt. She was the Chair of Computer Engineering Department- female section, Computers and Information Technology (CIT) College, Taif University, KSA for one year from 2015. She was the Chair of Computer Science Department - female section, CIT College, Taif University, KSA for 5 years. She has authored/edited more than 20 books with Elsevier, and Springer, and published more than 150 papers in repute journals.
Ashour is a Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier. She is an Editor-in-Chief for the International Journal of Synthetic Emotions (IJSE), IGI Global, US. She is an Associate Editor and reviewer in several journals. Her research interests include Biomedical Engineering, Computer- aided diagnosis systems, Image processing, Medical imaging, Machine learning, Optimization, Neutrosophic theory, Smart antenna, Direction of arrival estimation, and Targets tracking.