- 300 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
High-Performance Medical Image Processing
About this book
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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Information
Table of contents
- Cover Page
- Half Title Page
- Title Page
- Copyright Page
- Table of Contents
- Contributors
- Abbreviations
- Foreword
- Acknowledgment
- Preface
- 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
- Index