Medical Image Recognition  Segmentation and Parsing Book

Medical Image Recognition Segmentation and Parsing


  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Release Date : 2015-12-11
  • Genre: Computers
  • Pages : 542
  • ISBN 10 : 9780128026762

DOWNLOAD BOOK
Medical Image Recognition Segmentation and Parsing Excerpt :

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets Includes algorithms for recognizing and parsing of known anatomies for practical applications

Medical Image Recognition  Segmentation and Parsing Book

Medical Image Recognition Segmentation and Parsing


  • Author : Kevin Zhou
  • Publisher : Academic Press
  • Release Date : 2015-12-01
  • Genre: Uncategoriezed
  • Pages : 542
  • ISBN 10 : 0128025816

DOWNLOAD BOOK
Medical Image Recognition Segmentation and Parsing Excerpt :

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets Includes algorithms for recognizing and parsing of known anatomies for practical applications

Machine Learning and Medical Imaging Book

Machine Learning and Medical Imaging


  • Author : Guorong Wu
  • Publisher : Academic Press
  • Release Date : 2016-08-11
  • Genre: Technology & Engineering
  • Pages : 512
  • ISBN 10 : 9780128041147

DOWNLOAD BOOK
Machine Learning and Medical Imaging Excerpt :

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Handbook of Medical Image Computing and Computer Assisted Intervention Book

Handbook of Medical Image Computing and Computer Assisted Intervention


  • Author : Kevin Zhou
  • Publisher : Academic Press
  • Release Date : 2019-10-18
  • Genre: Computers
  • Pages : 1074
  • ISBN 10 : 9780128165867

DOWNLOAD BOOK
Handbook of Medical Image Computing and Computer Assisted Intervention Excerpt :

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention

Machine Learning in Medical Imaging Book

Machine Learning in Medical Imaging


  • Author : Qian Wang
  • Publisher : Springer
  • Release Date : 2017-09-06
  • Genre: Computers
  • Pages : 391
  • ISBN 10 : 9783319673899

DOWNLOAD BOOK
Machine Learning in Medical Imaging Excerpt :

This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

Finite Element Method and Medical Imaging Techniques in Bone Biomechanics Book

Finite Element Method and Medical Imaging Techniques in Bone Biomechanics


  • Author : Rabeb Ben Kahla
  • Publisher : John Wiley & Sons
  • Release Date : 2020-01-02
  • Genre: Science
  • Pages : 200
  • ISBN 10 : 9781786305183

DOWNLOAD BOOK
Finite Element Method and Medical Imaging Techniques in Bone Biomechanics Excerpt :

Digital models based on data from medical images have recently become widespread in the field of biomechanics. This book summarizes medical imaging techniques and processing procedures, both of which are necessary for creating bone models with finite element methods. Chapter 1 introduces the main principles and the application of the most commonly used medical imaging techniques. Chapter 2 describes the major methods and steps of medical image analysis and processing. Chapter 3 presents a brief review of recent studies on reconstructed finite element bone models, based on medical images. Finally, Chapter 4 reveals the digital results obtained for the main bone sites that have been targeted by finite element modeling in recent years.

Deep Learning for Medical Image Analysis Book

Deep Learning for Medical Image Analysis


  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Release Date : 2017-01-18
  • Genre: Computers
  • Pages : 458
  • ISBN 10 : 9780128104095

DOWNLOAD BOOK
Deep Learning for Medical Image Analysis Excerpt :

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Deep Learning and Convolutional Neural Networks for Medical Image Computing Book

Deep Learning and Convolutional Neural Networks for Medical Image Computing


  • Author : Le Lu
  • Publisher : Springer
  • Release Date : 2017-07-12
  • Genre: Computers
  • Pages : 326
  • ISBN 10 : 9783319429991

DOWNLOAD BOOK
Deep Learning and Convolutional Neural Networks for Medical Image Computing Excerpt :

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Hybrid Soft Computing for Image Segmentation Book

Hybrid Soft Computing for Image Segmentation


  • Author : Siddhartha Bhattacharyya
  • Publisher : Springer
  • Release Date : 2016-11-12
  • Genre: Computers
  • Pages : 321
  • ISBN 10 : 9783319472232

DOWNLOAD BOOK
Hybrid Soft Computing for Image Segmentation Excerpt :

This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.

Medical Image Computing and Computer Assisted Intervention     MICCAI 2020 Book

Medical Image Computing and Computer Assisted Intervention MICCAI 2020


  • Author : Anne L. Martel
  • Publisher : Springer Nature
  • Release Date : 2020-10-02
  • Genre: Computers
  • Pages : 785
  • ISBN 10 : 9783030597139

DOWNLOAD BOOK
Medical Image Computing and Computer Assisted Intervention MICCAI 2020 Excerpt :

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

Riemannian Geometric Statistics in Medical Image Analysis Book

Riemannian Geometric Statistics in Medical Image Analysis


  • Author : Xavier Pennec
  • Publisher : Academic Press
  • Release Date : 2019-09
  • Genre: Computers
  • Pages : 634
  • ISBN 10 : 9780128147252

DOWNLOAD BOOK
Riemannian Geometric Statistics in Medical Image Analysis Excerpt :

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: - The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs - Applications of statistics on manifolds and shape spaces in medical image computing - Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. - A complete reference covering both the foundations and state-of-the-art methods - Edited and authored by leading researchers in the field - Contains theory, examples, applications, and algorithms - Gives an overview of current research challenges and future applications

Deep Network Design for Medical Image Computing Book

Deep Network Design for Medical Image Computing


  • Author : Haofu Liao
  • Publisher : Academic Press
  • Release Date : 2022-09-01
  • Genre: Computers
  • Pages : 266
  • ISBN 10 : 9780128244036

DOWNLOAD BOOK
Deep Network Design for Medical Image Computing Excerpt :

Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems. Explains design principles of deep learning techniques for MIC Contains cutting-edge deep learning research on MIC Covers a broad range of MIC tasks, including the classification, detection, segmentation, registration, reconstruction and synthesis of medical images

Meta Learning With Medical Imaging and Health Informatics Applications Book

Meta Learning With Medical Imaging and Health Informatics Applications


  • Author : Hien Van Nguyen
  • Publisher : Academic Press
  • Release Date : 2022-09-30
  • Genre: Computers
  • Pages : 430
  • ISBN 10 : 9780323998529

DOWNLOAD BOOK
Meta Learning With Medical Imaging and Health Informatics Applications Excerpt :

Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks’ fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks. This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. The book comes with a GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly. First book on applying Meta Learning to medical imaging Pioneers in the field as contributing authors to explain the theory and its development Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly

Biomedical Image Synthesis and Simulation Book

Biomedical Image Synthesis and Simulation


  • Author : Ninon Burgos
  • Publisher : Academic Press
  • Release Date : 2022-06-30
  • Genre: Computers
  • Pages : 674
  • ISBN 10 : 9780128243503

DOWNLOAD BOOK
Biomedical Image Synthesis and Simulation Excerpt :

Biomedical Image Synthesis and Simulations: Methods and Applications presents the latest on basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. Sections introduce and describe the simulation and synthesis methods that were developed and successfully used within the last twenty years and give examples of successful applications of these methods. As the book provides a survey of all the commonly established approaches and more recent deep learning methods, it is highly suitable for graduate students and researchers in medical and biomedical imaging. Gives state-of-the-art methods in (bio)medical image synthesis Explains the principles (background) of image synthesis methods Presents the main applications of biomedical image synthesis methods