Overview

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results. With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques. Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented. Key features: Provides descriptions of different medical imaging modalities and their applications Discusses the basics and advanced aspects of parallel computing with different multicore architectures Expounds on the need for embedding data and task parallelism in different medical image processing techniques Presents helpful examples and case studies of the discussed methods This book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

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9781774637333

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Reviews

""A valuable window into different medical imaging modalities, different medical image processing techniques, parallel computing, and the need to embed data and task parallelism in medical image processing. The challenges in handling a massive amount of medical imaging data are vast and exciting. Scientists and researchers are working on them with enthusiasm, tenacity, and dedication to develop new parallelism methods, analysis, and provide new solutions to keep up with the ever-changing threats. In this new age of healthcare engineering, it is necessary to provide a way to discuss several issues of medical imaging processing and analysis techniques by both the professionals and students with state-of-the-art knowledge on the frontiers in medical sciences. This book is a good step in that direction."" Prof. Rajeev Srivastava, Professor and Head of the Department, Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India

"""A valuable window into different medical imaging modalities, different medical image processing techniques, parallel computing, and the need to embed data and task parallelism in medical image processing. The challenges in handling a massive amount of medical imaging data are vast and exciting. Scientists and researchers are working on them with enthusiasm, tenacity, and dedication to develop new parallelism methods, analysis, and provide new solutions to keep up with the ever-changing threats. In this new age of healthcare engineering, it is necessary to provide a way to discuss several issues of medical imaging processing and analysis techniques by both the professionals and students with state-of-the-art knowledge on the frontiers in medical sciences. This book is a good step in that direction."" Prof. Rajeev Srivastava, Professor and Head of the Department, Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India"

Author Information

Sanjay Saxena, PhD, is Assistant Professor in the Department of Computer Science and Engineering at the International Institute of Information Technology, Bhubaneswar, India. He has published several research papers in peer-reviewed international journals and conferences. He is a professional member of IEEE, ACM, New York Academy of Science, IAENG. Dr. Saxena earned his PhD from the Indian Institute of Technology (BHU), Varanasi, India, in High Performance Medical Image Processing. In addition, he completed postdoctorate research at the Perelman School of Medicine, University of Pennsylvania, USA, and worked on brain tumor (glioblastoma) segmentation and analysis. Sudip Paul, PhD, is Assistant Professor and Teacher In-Charge in the Department of Biomedical Engineering in the School of Technology at North-Eastern Hill University (NEHU), Shillong, India. Dr. Paul has published more than 90 papers in international journals and conferences and has also filed four patents. He has completed 10 book projects, and two are ongoing as editor and two as authored. Dr. Paul was awarded First Prize of the Sushruta Innovation Award 2011, sponsored by the Department of Science and Technology, Government of India. He has organized many workshops and conferences, the most significant of which are the IEEE International Conference on Computational Performance Evaluation 2020; 29th Annual Meeting of the Society for Neurochemistry, India; and IRBO/APRC Associate School 2017. Dr. Paul is a member of various societies and professional bodies, including APSN, ISN, IBRO, SNCI, SfN, IEEE, IAS. He has received many awards, including the World Federation of Neurology (WFN) traveling fellowship, Young Investigator Award, IBRO Travel Awardee, and ISN Travel Award. Dr. Paul has contributed his knowledge to various international journals as an editorial board member and has presented his research in the USA, Greece, France, South Africa, and Australia.

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