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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.
Sommario
Chapter 1 Parkinson's disease MRIs analysis using fuzzy clustering approach.- Chapter 2 Parkinson's disease MRIs analysis using neutrosophic segmentation approach.- Chapter 3 Parkinson's disease MRIs analysis using neutrosophic clustering approach.- Chapter 4 Brain tumor segmentation using type-2 neutrosophic thresholding approach.- Chapter 5 COVID-19 scan image segmentation using quantum-clustering approach.- Chapter 6 Empirical Analyses.
Info autore
PRITPAL SINGH, assistant professor in Central University of Rajasthan, India. He has an academic experience of more than 7 years.
He served as a Senior Postdoctoral Fellow in the Department of Electrical Engineering at the Taipei National University of Technology, Taiwan, from 2019-2020. He is working as an Adjunct Professor (Research) from November, 2020 in the Institute of Theoretical Physics, Jagiellonian University,
Poland. He is an active research member of Bio-Data Research Group (under TEAM-NET Program) in the Institute of Theoretical Physics, Jagiellonian University. His research interests include ambiguous set theory, soft computing, optimization algorithms (especially quantum-based optimization), time series forecasting, image analysis, fMRI data analysis, machine learning, mathematical modeling and simulation. He has published numerous papers in refereed SCI journals, conference proceedings, book chapters and book.
