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Medical Image Processing Using Ai
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Book Synopsis Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing by : Rohit Raja
Download or read book Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing written by Rohit Raja and published by CRC Press. This book was released on 2020-12-23 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management. Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology. This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems. This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning. FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field
Download or read book Medical Imaging written by K.C. Santosh and published by CRC Press. This book was released on 2019-08-20 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.
Book Synopsis Artificial Intelligence in Medical Imaging by : Erik R. Ranschaert
Download or read book Artificial Intelligence in Medical Imaging written by Erik R. Ranschaert and published by Springer. This book was released on 2019-01-29 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Book Synopsis Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing by : Rohit Raja
Download or read book Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing written by Rohit Raja and published by CRC Press. This book was released on 2020-12-22 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management. Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology. This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems. This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning. FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field
Book Synopsis Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging by : Kenji Suzuki
Download or read book Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging written by Kenji Suzuki and published by Springer. This book was released on 2018-01-09 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.
Book Synopsis Medical Image Processing Using AI by : Priyanka Sharma
Download or read book Medical Image Processing Using AI written by Priyanka Sharma and published by Academic Guru Publishing House. This book was released on 2024-04-19 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Medical Image Processing Using AI" provides a thorough review of the current breakthroughs, methodologies, and applications in medical imaging with artificial intelligence (AI). This book by leading medical imaging and AI researchers delves into the junction of these two fields, giving readers the insights and practical expertise to navigate AI-driven medical image analysis. The book covers medical imaging and AI's fundamental principles and methods, including image acquisition, preprocessing, feature extraction, and machine learning algorithms for medical image processing. Readers master the key principles and strategies needed to use AI in medical imaging via simple explanations and examples. As readers progress through the chapters, they are introduced to a diverse array of clinical applications and use cases where AI has made significant inroads, revolutionizing diagnostic workflows, treatment planning, and patient care across various medical specialties. Real-world case studies and examples illustrate how AI algorithms are being deployed in radiology, pathology, oncology, cardiology, and other fields to enhance diagnostic accuracy, improve treatment outcomes, and optimize clinical decision-making. Moreover, the book explores the ethical considerations, challenges, and future directions shaping the landscape of AI-driven medical image processing. From data privacy and algorithmic bias to regulatory frameworks and clinical integration, readers gain insight into the broader implications of AI in healthcare and the importance of responsible and equitable deployment of AI technologies.
Book Synopsis Medical Image Analysis by : Alejandro Frangi
Download or read book Medical Image Analysis written by Alejandro Frangi and published by Academic Press. This book was released on 2023-09-20 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing
Book Synopsis Deep Learning for Medical Image Analysis by : S. Kevin Zhou
Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-12-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is 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 are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest 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 cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Download or read book Medical Imaging written by K.C. Santosh and published by CRC Press. This book was released on 2019-08-20 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.
Book Synopsis Machine Learning and Medical Imaging by : Guorong Wu
Download or read book Machine Learning and Medical Imaging written by Guorong Wu and published by Academic Press. This book was released on 2016-08-11 with total page 512 pages. Available in PDF, EPUB and Kindle. Book 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
Book Synopsis Deep Learning in Medical Image Analysis by : Gobert Lee
Download or read book Deep Learning in Medical Image Analysis written by Gobert Lee and published by Springer Nature. This book was released on 2020-02-06 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.
Book Synopsis Applications of Artificial Intelligence in Medical Imaging by : Abdulhamit Subasi
Download or read book Applications of Artificial Intelligence in Medical Imaging written by Abdulhamit Subasi and published by Academic Press. This book was released on 2022-11-10 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET and ultrasound. The book discusses the use of AI for detection of several cancer types, including brain tumor, breast, pancreatic, rectal, lung colon, and skin. In addition, it explains how AI and deep learning techniques can be used to diagnose Alzheimer's, Parkinson's, COVID-19 and mental conditions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis. Discusses new deep learning algorithms for image analysis and how they are used for medical images Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes
Book Synopsis Artificial Intelligence in Medical Imaging by : Lia Morra
Download or read book Artificial Intelligence in Medical Imaging written by Lia Morra and published by CRC Press. This book was released on 2019-11-25 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Choice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective
Book Synopsis Hybrid Image Processing Methods for Medical Image Examination by : Venkatesan Rajinikanth
Download or read book Hybrid Image Processing Methods for Medical Image Examination written by Venkatesan Rajinikanth and published by CRC Press. This book was released on 2021-01-29 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing
Book Synopsis Deep Learning Applications in Medical Imaging by : Saxena, Sanjay
Download or read book Deep Learning Applications in Medical Imaging written by Saxena, Sanjay and published by IGI Global. This book was released on 2020-10-16 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.
Book Synopsis Convolutional Neural Networks for Medical Image Processing Applications by : Saban Ozturk
Download or read book Convolutional Neural Networks for Medical Image Processing Applications written by Saban Ozturk and published by CRC Press. This book was released on 2022-12-23 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits. While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.
Book Synopsis Computer Vision In Medical Imaging by : Chi Hau Chen
Download or read book Computer Vision In Medical Imaging written by Chi Hau Chen and published by World Scientific. This book was released on 2013-11-18 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: The major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of diseases. Computer vision can exploit texture, shape, contour and prior knowledge along with contextual information from image sequence and provide 3D and 4D information that helps with better human understanding. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much needed quantitative information not easily available by trained human specialists. The aim of the book is for both medical imaging professionals to acquire and interpret the data, and computer vision professionals to provide enhanced medical information by using computer vision techniques. The final objective is to benefit the patients without adding to the already high medical costs.
