High-Performance Medical Image Processing

"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."-- Provided by publisher 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 com Cover 1 Half Title 2 Title Page 4 Copyright Page 5 About the Editors 8 Table of Contents 10 Contributors 12 Abbreviations 14 Foreword 18 Acknowledgment 20 Preface 22 1. Basic Understanding of Medical Imaging Modalities 30 2. Parallel Computing 48 3. Basic Understanding of Medical Image Processing 80 4. Multicore Architectures and Their Applications in Image Processing 92 5. Machine Learning Applications in Medical Image Processing 124 6. Conventional and Advanced Magnetic Resonance Imaging Methods 160 7. Detection and Classification of Brain Tumors from MRI Images by Different Classifiers 174 8. Tumor Detection Based on 3D Segmentation Using Region of Interest 210 9. Advances in Parallel Techniques for Hyperspectral Image Processing 226 10. Case Study: Pulmonary Nodule Detection Using Image Processing and Statistical Networks 252 11. Embedding Parallelism in Image Processing Techniques and Its Applications 262 12. High‐Performance Computing and Its Requirements in Deep Learning 284 Index 318 3D,segmentation;,advanced,classifiers;,‍brain,tumors;,deep,learning;,‍embedding,parallelism;,high-performance,computing;,‍hyperspectral,image,processing;,machine,learning,applications;,‍magnetic,resonance,imaging,methods;,medical,image,processing;,‍medical,imaging,modalities;,multicore,architectures;,‍parallel,computing;,pulmonary,nodule,detection;,‍statistical,networks;,tumor,detection 3D segmentation,advanced classifiers,‍brain tumors,deep learning,‍embedding parallelism,high-performance computing,‍hyperspectral image processing,machine learning applications,‍magnetic resonance imaging methods,medical image processing,‍medical imaging modalities,multicore architectures,‍parallel computing,pulmonary nodule detection,‍statistical networks,tumor detection