Medical Imaging and Computer-Aided Diagnosis (eBook)
XV, 584 Seiten
Springer Nature Singapore (Verlag)
978-981-16-6775-6 (ISBN)
This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging.
Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human-computer interaction, databases, and performance evaluation.
Dr. Ruidan Su received his MSc in Software Engineering from Northeastern University, China in 2010, and his Ph.D degree in Computer Application Technology from Northeastern University, China in 2014. He was an Assistance Professor of Shanghai Advanced Research Institute, Chinese Academy of Sciences during 2015-2020. He joined Department of Computer Sciences and Engineering, Shanghai Jiao Tong University from 2021. His fields of science include artificial intelligence, Machine learning, Bidirectional intelligence, Computational Intelligence, System Optimization, Multi Population Genetic Algorithm.
Dr. Su is an IEEE Senior Member. He has published 26 papers in referred journals, conference proceedings. He was the Founder & Editor-in-Chief of Journal of Computational Intelligence and Electronic Systems published by American Scientific Publisher from 2012-2016. He is an Associate Editor for the Journal of Granular Computing Published by Springer, an Associate Editor for the Journal of Intelligent & Fuzzy Systems published by IOS Press, a Review Board Member for Applied Intelligence.
Dr. Su was the guest editor for Multimedia Tools and Applications by Springer for Special Issue on Practical Augmented Reality (AR) Technology and its Applications and Special Issue on Deep Processing of Multimedia Data, a Proceeding Editor for the Proceeding of 2018 & 2019 & 2020 International Conference on Image and Video Processing, and Artificial Intelligence (IVPAI 2018 & 2019 & 2020, Published by SPIE). He was a Conference Chair for 2018 & 2019 International conference on Image, Video Processing and Artificial Intelligence, a conference Chair for 2018 3rd International Conference on Computer, Communication and Computational Sciences, a Conference Program Committee Member for 18th International Conference on Machine Learning and Cybernetics
Dr. Ruidan Su has been a reviewer for several leading journals, such as Information Sciences, IEEE Transactions on Cybernetics, IEEE Access, Applied Intelligence, International Journal of Pattern Recognition and Artificial Intelligence, Knowledge and Information Systems, Multimedia Tools and Application, The Journal of Supercomputing, Concurrency and Computation: Practice and Experience, Electronic Commerce Research
Prof. Yu-Dong Zhang received his PhD degree from Southeast University in 2010. He worked as a postdoc from 2010 to 2012 in Columbia University, USA, and as an assistant research scientist from 2012 to 2013 at Research Foundation of Mental Hygiene (RFMH), USA. He served as a full professor from 2013 to 2017 in Nanjing Normal University, where he was the director and founder of Advanced Medical Image Processing Group in NJNU. Now he serves as Professor in Department of Informatics, University of Leicester, UK.
He was included in 'Most Cited Chinese researchers (Computer Science)' by Elsevier from 2014 to 2018. He was the 2019 recipient of 'Highly Cited Researcher' by Web of Science. He won 'Emerald Citation of Excellence 2017' and 'MDPI Top 10 Most Cited Papers 2015'. He was included in 'Top Scientist' in Guide2Research. He published over 160 papers, including 16 'ESI Highly Cited Papers', and 2 'ESI Hot Papers'. His citation reached 10096 in Google Scholar, and 5362 in Web of Science.
He is the fellow of IET (FIET), and the senior members of IEEE and ACM. He is the editor of Scientific Reports, IEEE Transactions on Circuits and Systems for Video Technology, etc. He served as the (leading) guest editor of Information Fusion, Neural Networks, IEEE Transactions on Intelligent Transportation Systems, etc. He has conducted many successful industrial projects and academic grants from NSFC, NIH, Royal Society, and British Council.
Dr. Han Liu is currently an Associate Researcher in Machine Learning in the College of Computer Science and Software Engineering at the Shenzhen University. He has previously been a Research Associate in Data Science in the School of Computer Science and Informatics at the Cardiff University and has also been a Research Associate in Computational Intelligence in the School of Computing at the University of Portsmouth. He received a BSc in Computing from University of Portsmouth in 2011, an MSc in Software Engineering from University of Southampton in 2012, and a PhD in Machine Learning from University of Portsmouth in 2015.
His research interests are in artificial intelligence in general and machine learning in particular. His other related areas include sentiment analysis, pattern recognition, intelligent systems, big data, granular computing, and computational intelligence.
He has published two research monographs in Springer and over 70 papers in areas such as data mining, machine learning and intelligent systems. He has been an Associate Editor for the Granular Computing Journal and has been a reviewer for several leading journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Fuzzy Systems and Information Sciences (Elsevier). He has also been appointed as a programme chair for the 2020 International Conference on Image, Video Processing and Artificial Intelligence and the 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis. In addition, he was awarded Member of the Institution of Engineering and Technology (IET) with designatory letters MIET in February 2016.
This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human-computer interaction, databases, and performance evaluation.
| Erscheint lt. Verlag | 20.1.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Electrical Engineering | Lecture Notes in Electrical Engineering |
| Zusatzinfo | XV, 584 p. 221 illus., 179 illus. in color. |
| Sprache | englisch |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Medizin / Pharmazie ► Pflege | |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Medizintechnik | |
| Schlagworte | Artificial Intelligence • Big Data • Computer Aided Diagnosis • Medical Imaging • MICAD 2021 |
| ISBN-10 | 981-16-6775-6 / 9811667756 |
| ISBN-13 | 978-981-16-6775-6 / 9789811667756 |
| Haben Sie eine Frage zum Produkt? |
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