This volume helps to meet the challenge processing of medical images 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.
This volume helps to meet the challenge processing of medical images 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.
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.
Inhaltsangabe
1. Basic Understanding of Medical Imaging Modalities 2. Parallel Computing 3. Basic Understanding of Medical Image Processing 4. Multicore Architectures and Their Applications in Image Processing 5. Machine Learning Applications in Medical Image Processing 6. Conventional and Advanced Magnetic Resonance Imaging Methods 7. Detection and Classification of Brain Tumors from MRI Images by Different Classifiers 8. Tumor Detection Based on 3D Segmentation Using Region of Interest 9. Advances in Parallel Techniques for Hyperspectral Image Processing 10. Case Study: Pulmonary Nodule Detection Using Image Processing and Statistical Networks 11. Embedding Parallelism in Image Processing Techniques and Its Applications 12. High-Performance Computing and Its Requirements in Deep Learning
1. Basic Understanding of Medical Imaging Modalities 2. Parallel Computing 3. Basic Understanding of Medical Image Processing 4. Multicore Architectures and Their Applications in Image Processing 5. Machine Learning Applications in Medical Image Processing 6. Conventional and Advanced Magnetic Resonance Imaging Methods 7. Detection and Classification of Brain Tumors from MRI Images by Different Classifiers 8. Tumor Detection Based on 3D Segmentation Using Region of Interest 9. Advances in Parallel Techniques for Hyperspectral Image Processing 10. Case Study: Pulmonary Nodule Detection Using Image Processing and Statistical Networks 11. Embedding Parallelism in Image Processing Techniques and Its Applications 12. High-Performance Computing and Its Requirements in Deep Learning
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