December 3, 2020

Download Ebook Free Medical Image Recognition, Segmentation And Parsing

Medical Image Recognition, Segmentation and Parsing

Medical Image Recognition, Segmentation and Parsing
Author : S. Kevin Zhou
Publisher : Academic Press
Release Date : 2015-12-11
Category : Computers
Total pages :542
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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

Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention
Author : S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger
Publisher : Academic Press
Release Date : 2019-10-18
Category : Computers
Total pages :1072
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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

Machine Learning in Medical Imaging
Author : Qian Wang,Yinghuan Shi,Heung-Il Suk,Kenji Suzuki
Publisher : Springer
Release Date : 2017-09-06
Category : Computers
Total pages :391
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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

Finite Element Method and Medical Imaging Techniques in Bone Biomechanics
Author : Rabeb Ben Kahla,Abdelwahed Barkaoui,Tarek Merzouki
Publisher : John Wiley & Sons
Release Date : 2020-01-02
Category : Science
Total pages :200
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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

Deep Learning for Medical Image Analysis
Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Release Date : 2017-01-18
Category : Computers
Total pages :458
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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

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
Publisher : Academic Press
Release Date : 2016-08-11
Category : Technology & Engineering
Total pages :512
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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

Decision Forests for Computer Vision and Medical Image Analysis

Decision Forests for Computer Vision and Medical Image Analysis
Author : Antonio Criminisi,J Shotton
Publisher : Springer Science & Business Media
Release Date : 2013-01-30
Category : Computers
Total pages :368
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This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.

Medical Computer Vision

Medical Computer Vision
Author : Bjoern Menze,Georg Langs,Zhuowen Tu,Antonio Criminisi
Publisher : Springer
Release Date : 2011-02-02
Category : Computers
Total pages :226
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This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2010, held in Beijing, China, in September 2010 as a satellite event of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2010. The 10 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 38 initial submissions. The papers explore the use of modern image recognition technology in tasks such as semantic anatomy parsing, automatic segmentation and quantification, anomaly detection and categorization, data harvesting, semantic navigation and visualization, data organization and clustering, and general-purpose automatic understanding of medical images.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Author : Anne L. Martel
Publisher : Springer Nature
Release Date : 2020
Category :
Total pages :129
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Digital Image Processing for Medical Applications

Digital Image Processing for Medical Applications
Author : Geoff Dougherty
Publisher : Cambridge University Press
Release Date : 2009-04-09
Category : Computers
Total pages :447
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Hands-on text for a first course aimed at end-users, focusing on concepts, practical issues and problem solving.

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
Author : Maxime Descoteaux,Lena Maier-Hein,Alfred Franz,Pierre Jannin,D. Louis Collins,Simon Duchesne
Publisher : Springer
Release Date : 2017-09-03
Category : Computers
Total pages :811
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The three-volume set LNCS 10433, 10434, and 10435 constitutes the refereed proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, held inQuebec City, Canada, in September 2017. The 255 revised full papers presented were carefully reviewed and selected from 800 submissions in a two-phase review process. The papers have been organized in the following topical sections: Part I: atlas and surface-based techniques; shape and patch-based techniques; registration techniques, functional imaging, connectivity, and brain parcellation; diffusion magnetic resonance imaging (dMRI) and tensor/fiber processing; and image segmentation and modelling. Part II: optical imaging; airway and vessel analysis; motion and cardiac analysis; tumor processing; planning and simulation for medical interventions; interventional imaging and navigation; and medical image computing. Part III: feature extraction and classification techniques; and machine learning in medical image computing.

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author : Guorong Wu,Daoqiang Zhang,Luping Zhou
Publisher : Springer
Release Date : 2014-09-05
Category : Computers
Total pages :332
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This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Medical Imaging, MLMI 2014, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, in Cambridge, MA, USA, in September 2014. The 40 contributions included in this volume were carefully reviewed and selected from 70 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

Biomedical Image Understanding

Biomedical Image Understanding
Author : Joo-Hwee Lim,Sim-Heng Ong,Wei Xiong
Publisher : John Wiley & Sons
Release Date : 2015-02-16
Category : Medical
Total pages :496
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With contributions from experts in China, France, Italy, Japan, Singapore, the United Kingdom, and the United States, Biomedical Image Understanding: • Addresses motion tracking and knowledge-based systems, two areas which are not covered extensively elsewhere in a biomedical context • Describes important clinical applications, such as virtual colonoscopy, ocular disease diagnosis, and liver tumor detection • Contains twelve self-contained chapters, each with an introduction to basic concepts, principles, and methods, and a case study or application

Advancement of Machine Intelligence in Interactive Medical Image Analysis

Advancement of Machine Intelligence in Interactive Medical Image Analysis
Author : Om Prakash Verma,Sudipta Roy,Subhash Chandra Pandey,Mamta Mittal
Publisher : Springer Nature
Release Date : 2019-12-11
Category : Computers
Total pages :329
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The book discusses major technical advances and research findings in the field of machine intelligence in medical image analysis. It examines the latest technologies and that have been implemented in clinical practice, such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy. This book provides insights into the basic science involved in processing, analysing, and utilising all aspects of advanced computational intelligence in medical decision-making based on medical imaging.

Information Processing in Medical Imaging

Information Processing in Medical Imaging
Author : Gábor Székely,Horst K. Hahn
Publisher : Springer Science & Business Media
Release Date : 2011-06-29
Category : Computers
Total pages :787
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This book constitutes the refereed proceedings of the 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011, held at Kloster Irsee, Germany, in July 2011. The 24 full papers and 39 poster papers included in this volume were carefully reviewed and selected from 224 submissions. The papers are organized in topical sections on segmentation, statistical methods, shape analysis, registration, diffusion imaging, disease progression modeling, and computer aided diagnosis. The poster sessions deal with segmentation, shape analysis, statistical methods, image reconstruction, microscopic image analysis, computer aided diagnosis, diffusion imaging, functional brain analysis, registration and other related topics.