Neuro-oncology broadly encompasses life-threatening brain and spinal cord malignancies, including primary lesions and lesions metastasizing to the central nervous system. It is well suited for diagnosis, classification, and prognosis as well as assessing treatment response. Radiomics and Radiogenomics (R-n-R) have become two central pillars in precision medicine for neuro-oncology.Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while Radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Due to the exponential progress of different computational algorithms, AI methods are composed to advance the precision of diagnostic and therapeutic approaches in neuro-oncology.The field of radiomics has been and definitely will remain at the lead of this emerging discipline due to its efficiency in the field of neuro-oncology. Several AI approaches applied to conventional and advanced medical imaging data from the perspective of radiomics are very efficient for tasks such as survival prediction, heterogeneity analysis of cancer, pseudo progression analysis, and infiltrating tumors. Radiogenomics advances our understanding and knowledge of cancer biology, letting noninvasive sampling of the molecular atmosphere with high spatial resolution along with a systems-level understanding of causal heterogeneous molecular and cellular processes. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility across large multi-institutional cohorts.Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm provides readers with a broad and detailed framework for R-n-R approaches with AI in neuro-oncology, the description of cancer biology and genomics study of cancer, and the methods usually implemented for analyzing. Readers will also learn about the current solutions R-n-R can offer for personalized treatments of patients, limitations, and prospects. There is comprehensive coverage of information based on radiomics, radiogenomics, cancer biology, and medical image analysis viewpoints on neuro-oncology, so this in-depth coverage is divided into two Volumes.Volume 1: Radiogenomics Flow Using Artificial Intelligence provides coverage of genomics and molecular study of brain cancer, medical imaging modalities and analysis in neuro-oncology, and prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications provides coverage of imaging signatures for brain cancer molecular characteristics, clinical applications of R-n-R in neuro-oncology, and Machine Learning and Deep Learning AI approaches for R-n-R in neuro-oncology.- Includes coverage on the foundational concepts of the emerging fields of radiomics and radiogenomics- Covers neural engineering modeling and AI algorithms for the imaging, diagnosis, and predictive modeling of neuro-oncology- Presents crucial technologies and software platforms, along with advanced brain imaging techniques such as quantitative imaging using CT, PET, and MRI- Provides in-depth technical coverage of computational modeling techniques and applied mathematics for brain tumor segmentation and radiomics features such as extraction and selection

Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm, Volume One: Radiogenomics Flow Using Artificial Intelligence broadly encompasses the study of life-threatening brain and spinal cord malignancies, including primary lesions and those metastasizing to the central nervous system. Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Sections in this book discuss several AI approaches that have been applied to conventional and advanced medical imaging data. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility.Includes coverage on the foundational concepts of the emerging fields of radiomics and radiogenomicsCovers neural engineering modeling and AI algorithms for the imaging, diagnosis, and predictive modeling of neuro-oncologyPresents crucial technologies and software platforms, along with advanced brain imaging techniques such as quantitative imaging using CT, PET, and MRIProvides in-depth technical coverage of computational modeling techniques and applied mathematics for brain tumor segmentation and radiomics features such as extraction and selection

Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm-Volume 2: Genetics and Clinical Applications provides readers with a broad and detailed framework for radiomics and radiogenomics (R-n-R) approaches with AI in neuro-oncology. It delves into the study of cancer biology and genomics, presenting methods and techniques for analyzing these elements. The book also highlights current solutions that R-n-R can offer for personalized patient treatments, as well as discusses the limitations and future prospects of AI technologies.Volume 1: Radiogenomics Flow Using Artificial Intelligence covers the genomics and molecular study of brain cancer, medical imaging modalities and their analysis in neuro-oncology, and the development of prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications extends the discussion to imaging signatures that correlate with molecular characteristics of brain cancer, clinical applications of R-n-R in neuro-oncology, and the use of Machine Learning and Deep Learning approaches for R-n-R in neuro-oncology.

Includes coverage of foundational concepts of the emerging fields of Radiomics and RadiogenomicsCovers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutationsPresents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosisProvides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma

Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm—Volume 2: Genetics and Clinical Applications provides readers with a broad and detailed framework for radiomics and radiogenomics (R-n-R) approaches with AI in neuro-oncology. It delves into the study of cancer biology and genomics, presenting methods and techniques for analyzing these elements. The book also highlights current solutions that R-n-R can offer for personalized patient treatments, as well as discusses the limitations and future prospects of AI technologies.Volume 1: Radiogenomics Flow Using Artificial Intelligence covers the genomics and molecular study of brain cancer, medical imaging modalities and their analysis in neuro-oncology, and the development of prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications extends the discussion to imaging signatures that correlate with molecular characteristics of brain cancer, clinical applications of R-n-R in neuro-oncology, and the use of Machine Learning and Deep Learning approaches for R-n-R in neuro-oncology.- Includes coverage of foundational concepts of the emerging fields of Radiomics and Radiogenomics- Covers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutations- Presents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosis- Provides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma

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.

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.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

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 applicationsDiscusses the basics and advanced aspects of parallel computing with different multicore architecturesExpounds on the need for embedding data and task parallelism in different medical image processing techniquesPresents helpful examples and case studies of the discussed methodsThis 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.

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 applicationsDiscusses the basics and advanced aspects of parallel computing with different multicore architecturesExpounds on the need for embedding data and task parallelism in different medical image processing techniquesPresents helpful examples and case studies of the discussed methodsThis 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.

Neuro-oncology broadly encompasses life-threatening brain and spinal cord malignancies, including primary lesions and lesions metastasizing to the central nervous system. It is well suited for diagnosis, classification, and prognosis as well as assessing treatment response. Radiomics and Radiogenomics (R-n-R) have become two central pillars in precision medicine for neuro-oncology.Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while Radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Due to the exponential progress of different computational algorithms, AI methods are composed to advance the precision of diagnostic and therapeutic approaches in neuro-oncology.The field of radiomics has been and definitely will remain at the lead of this emerging discipline due to its efficiency in the field of neuro-oncology. Several AI approaches applied to conventional and advanced medical imaging data from the perspective of radiomics are very efficient for tasks such as survival prediction, heterogeneity analysis of cancer, pseudo progression analysis, and infiltrating tumors. Radiogenomics advances our understanding and knowledge of cancer biology, letting noninvasive sampling of the molecular atmosphere with high spatial resolution along with a systems-level understanding of causal heterogeneous molecular and cellular processes. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility across large multi-institutional cohorts.Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm provides readers with a broad and detailed framework for R-n-R approaches with AI in neuro-oncology, the description of cancer biology and genomics study of cancer, and the methods usually implemented for analyzing. Readers will also learn about the current solutions R-n-R can offer for personalized treatments of patients, limitations, and prospects. There is comprehensive coverage of information based on radiomics, radiogenomics, cancer biology, and medical image analysis viewpoints on neuro-oncology, so this in-depth coverage is divided into two Volumes.Volume 1: Radiogenomics Flow Using Artificial Intelligence provides coverage of genomics and molecular study of brain cancer, medical imaging modalities and analysis in neuro-oncology, and prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications provides coverage of imaging signatures for brain cancer molecular characteristics, clinical applications of R-n-R in neuro-oncology, and Machine Learning and Deep Learning AI approaches for R-n-R in neuro-oncology.- Includes coverage on the foundational concepts of the emerging fields of radiomics and radiogenomics- Covers neural engineering modeling and AI algorithms for the imaging, diagnosis, and predictive modeling of neuro-oncology- Presents crucial technologies and software platforms, along with advanced brain imaging techniques such as quantitative imaging using CT, PET, and MRI- Provides in-depth technical coverage of computational modeling techniques and applied mathematics for brain tumor segmentation and radiomics features such as extraction and selection

Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm, Volume One: Radiogenomics Flow Using Artificial Intelligence broadly encompasses the study of life-threatening brain and spinal cord malignancies, including primary lesions and those metastasizing to the central nervous system. Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Sections in this book discuss several AI approaches that have been applied to conventional and advanced medical imaging data. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility.Includes coverage on the foundational concepts of the emerging fields of radiomics and radiogenomicsCovers neural engineering modeling and AI algorithms for the imaging, diagnosis, and predictive modeling of neuro-oncologyPresents crucial technologies and software platforms, along with advanced brain imaging techniques such as quantitative imaging using CT, PET, and MRIProvides in-depth technical coverage of computational modeling techniques and applied mathematics for brain tumor segmentation and radiomics features such as extraction and selection

Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm-Volume 2: Genetics and Clinical Applications provides readers with a broad and detailed framework for radiomics and radiogenomics (R-n-R) approaches with AI in neuro-oncology. It delves into the study of cancer biology and genomics, presenting methods and techniques for analyzing these elements. The book also highlights current solutions that R-n-R can offer for personalized patient treatments, as well as discusses the limitations and future prospects of AI technologies.Volume 1: Radiogenomics Flow Using Artificial Intelligence covers the genomics and molecular study of brain cancer, medical imaging modalities and their analysis in neuro-oncology, and the development of prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications extends the discussion to imaging signatures that correlate with molecular characteristics of brain cancer, clinical applications of R-n-R in neuro-oncology, and the use of Machine Learning and Deep Learning approaches for R-n-R in neuro-oncology.

Includes coverage of foundational concepts of the emerging fields of Radiomics and RadiogenomicsCovers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutationsPresents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosisProvides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma

Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm—Volume 2: Genetics and Clinical Applications provides readers with a broad and detailed framework for radiomics and radiogenomics (R-n-R) approaches with AI in neuro-oncology. It delves into the study of cancer biology and genomics, presenting methods and techniques for analyzing these elements. The book also highlights current solutions that R-n-R can offer for personalized patient treatments, as well as discusses the limitations and future prospects of AI technologies.Volume 1: Radiogenomics Flow Using Artificial Intelligence covers the genomics and molecular study of brain cancer, medical imaging modalities and their analysis in neuro-oncology, and the development of prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications extends the discussion to imaging signatures that correlate with molecular characteristics of brain cancer, clinical applications of R-n-R in neuro-oncology, and the use of Machine Learning and Deep Learning approaches for R-n-R in neuro-oncology.- Includes coverage of foundational concepts of the emerging fields of Radiomics and Radiogenomics- Covers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutations- Presents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosis- Provides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma

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.

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.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

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 applicationsDiscusses the basics and advanced aspects of parallel computing with different multicore architecturesExpounds on the need for embedding data and task parallelism in different medical image processing techniquesPresents helpful examples and case studies of the discussed methodsThis 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.

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 applicationsDiscusses the basics and advanced aspects of parallel computing with different multicore architecturesExpounds on the need for embedding data and task parallelism in different medical image processing techniquesPresents helpful examples and case studies of the discussed methodsThis 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.