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.

在合理的時間範圍內並且具有高清晰度的處理醫學影像是非常具有挑戰性的。本書通過提供醫學影像模態、處理、高性能計算以及在醫學影像處理技術中嵌入並行性以實現高效和快速結果的需求的全面概述，有助於應對這一挑戰。

《高性能醫學影像處理》由來自著名實驗室和教育機構的研究人員貢獻，提供了有關醫學影像處理技術、並行計算技術以及在不同影像處理技術中嵌入並行性的重要信息。本書的一個重要特點是對醫學影像處理問題中並行算法的全面回顧。該卷介紹了相關的理論框架和該領域的最新實證研究結果，並提供了有關各種高性能技術的詳細描述。

討論的主題包括並行計算、多核架構及其在影像處理中的應用、機器學習應用、傳統和先進的磁共振成像方法、高光譜影像處理、用於分割二維切片以進行三維觀察的算法等。案例研究，例如癌症腫瘤的檢測，對所提供的信息進行了詳細闡述。

主要特點：
- 提供不同醫學影像模態及其應用的描述
- 討論不同多核架構下並行計算的基礎和高級方面
- 闡述在不同醫學影像處理技術中嵌入數據和任務並行性的需求
- 提供所討論方法的有用示例和案例研究

本書對於從事醫療工程、醫學影像技術、機器學習和深度學習應用等領域的專業人士、研究人員和學生非常有價值。同時，它也適用於基於人工智能、並行計算、高性能計算以及機器學習及其在醫學影像中的應用的計算機工程、生物醫學工程和電氣工程課程。

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.

Sanjay Saxena博士是印度Bhubaneswar國際資訊技術學院計算機科學與工程系的助理教授。他在同行評審的國際期刊和會議上發表了多篇研究論文。他是IEEE、ACM、紐約科學院和IAENG的專業會員。Saxena博士在印度Varanasi的印度理工學院（BHU）獲得了高性能醫學影像處理的博士學位。此外，他在賓夕法尼亞大學佩雷爾曼醫學院進行了博士後研究，並在腦腫瘤（膠母細胞瘤）分割和分析方面進行了工作。

Sudip Paul博士是印度Shillong東北山地大學（NEHU）技術學院生物醫學工程系的助理教授和教師負責人。Paul博士在國際期刊和會議上發表了90多篇論文，並申請了四項專利。他已完成10個圖書項目，其中兩個作為編輯進行中，另外兩個作為作者進行中。Paul博士獲得了由印度科學與技術部贊助的2011年Sushruta創新獎一等獎。他組織了許多研討會和會議，其中最重要的是2020年IEEE國際計算性能評估會議、印度神經化學學會第29屆年會和IRBO/APRC聯合學校2017。Paul博士是多個學會和專業組織的成員，包括APSN、ISN、IBRO、SNCI、SfN、IEEE和IAS。他獲得了許多獎項，包括世界神經學聯合會（WFN）旅行獎學金、年輕研究者獎、IBRO旅行獎和ISN旅行獎。Paul博士作為編輯委員會成員向各種國際期刊貢獻了他的知識，並在美國、希臘、法國、南非和澳大利亞發表了他的研究成果。