Neutrosophic Set in Medical Image Analysis

Erscheinungsjahr 2019
ISBN: 978-0-12-818148-5
Verlag: William Andrew Publishing

Buch, Englisch, 370 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 750 g

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.

Autoren/Hrsg.

Guo, Yanhui

Ashour, Amira S.

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware

Inhaltsverzeichnis

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

Über Autor(innen)

Ashour, Amira S.
Amira S. Ashour is an Assistant Professor and Head of Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Tanta University, Egypt. She is a member in the Research and Development Unit, Faculty of Engineering, Tanta University, Egypt. She received the B.Eng. degree in Electrical Engineering from Faculty of Engineering, Tanta University, Egypt in 1997, M.Sc. in Image Processing in 2001 and Ph.D. in Smart Antenna in 2005 from Faculty of Engineering, Tanta University, Egypt. Ashour has been the Vice Chair of Computer Engineering Department, Computers and Information Technology College, Taif University, KSA for one year from 2015. She has been the vice chair of CS department, CIT college, Taif University, KSA for 5 years. Her research interests are Smart antenna, Direction of arrival estimation, Targets tracking, Image processing, Medical imaging, Machine learning, Biomedical Systems, Pattern recognition, Image analysis, Computer vision, Computer-aided detection and diagnosis systems, Optimization, and Neutrosophic theory. She has 15 books and about 150 published journal papers. She is an Editor-in-Chief for the International Journal of Synthetic Emotions (IJSE), IGI Global, US.

Guo, Yanhui
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.