Medical Image Recognition Segmentation and Parsing

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

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  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Pages : 542 pages
  • ISBN : 0128026766
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMedical 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 : 11 December 2015
GET THIS BOOKMedical Image Recognition, Segmentation and Parsing

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,

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 : 18 October 2019
GET THIS BOOKHandbook of Medical Image Computing and Computer Assisted Intervention

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

Riemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis
  • Author : Xavier Pennec,Stefan Sommer,Tom Fletcher
  • Publisher : Academic Press
  • Release : 01 September 2019
GET THIS BOOKRiemannian Geometric Statistics in Medical Image Analysis

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

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
  • Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
  • Publisher : Academic Press
  • Release : 11 August 2016
GET THIS BOOKMachine Learning and Medical Imaging

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

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Qian Wang,Yinghuan Shi,Heung-Il Suk,Kenji Suzuki
  • Publisher : Springer
  • Release : 06 September 2017
GET THIS BOOKMachine Learning in Medical Imaging

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

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 : 06 December 2019
GET THIS BOOKFinite Element Method and Medical Imaging Techniques in Bone Biomechanics

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

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
  • Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
  • Publisher : Academic Press
  • Release : 18 January 2017
GET THIS BOOKDeep Learning for Medical Image Analysis

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

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 : 03 September 2017
GET THIS BOOKMedical Image Computing and Computer Assisted Intervention − MICCAI 2017

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/

Deep Learning for COVID Image Analysis

Deep Learning for COVID Image Analysis
  • Author : Hayit Greenspan,S. Kevin Zhou
  • Publisher : Academic Press
  • Release : 15 October 2021
GET THIS BOOKDeep Learning for COVID Image Analysis

Medical imaging is playing a role in the fight against COVID-19, in some countries as a key tool, from the screening and diagnosis through the entire treatment procedure. The extraordinarily rapid spread of this pandemic has demonstrated that a new disease entity with a subset of relatively unique characteristics can pose a major new clinical challenge that requires new diagnostic tools in imaging. The AI/Deep Learning Imaging community has shown in many recent publications that rapidly developed AI-based automated

Medical Computer Vision

Medical Computer Vision
  • Author : Bjoern Menze,Georg Langs,Zhuowen Tu,Antonio Criminisi
  • Publisher : Springer
  • Release : 02 February 2011
GET THIS BOOKMedical Computer Vision

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

Deep Learning in Medical Image Analysis

Deep Learning in Medical Image Analysis
  • Author : Gobert Lee,Hiroshi Fujita
  • Publisher : Springer Nature
  • Release : 06 February 2020
GET THIS BOOKDeep Learning in Medical Image Analysis

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical

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 : 11 December 2019
GET THIS BOOKAdvancement of Machine Intelligence in Interactive Medical Image Analysis

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.

Video Cataloguing

Video Cataloguing
  • Author : Guangyu Gao,Chi Harold Liu
  • Publisher : CRC Press
  • Release : 27 October 2015
GET THIS BOOKVideo Cataloguing

The arrival of the digital age has created the need to be able to store, manage, and digitally use an ever-increasing amount of video and audio material. Thus, video cataloguing has emerged as a requirement of the times. Video Cataloguing: Structure Parsing and Content Extraction explains how to efficiently perform video structure analysis as well

Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing

Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing
  • Author : Tong Zhang,C.C. Jay Kuo
  • Publisher : Springer Science & Business Media
  • Release : 09 March 2013
GET THIS BOOKContent-Based Audio Classification and Retrieval for Audiovisual Data Parsing

Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing is an up-to-date overview of audio and video content analysis. Included is extensive treatment of audiovisual data segmentation, indexing and retrieval based on multimodal media content analysis, and content-based management of audio data. In addition to the commonly studied audio types such as speech and music, the authors have included hybrid types of sounds that contain more than one kind of audio component such as speech or environmental sound with music