Provides a comprehensive overview of machine learning and deep learning techniques for biomedical imaging Includes thoracic imaging, abdominal imaging, brain imaging, and retinal imaging Covers new and emerging methods in machine learning Features contributions from leading experts Presents tools to improve computer aided diagnosis

Today's healthcare organizations must focus on a lot more than just the health of their clients. The infrastructure it takes to support clinical-care delivery continues to expand, with information technology being one of the most significant contributors to that growth. As companies have become more dependent on technology for their clinical, administrative, and financial functions, their IT departments and expenditures have had to scale quickly to keep up. However, as technology demands have increased, so have the options for reliable infrastructure for IT applications and data storage. The one that has taken center stage over the past few years is cloud computing. Healthcare researchers are moving their efforts to the cloud because they need adequate resources to process, store, exchange, and use large quantities of medical data. Cloud Computing in Medical Imaging covers the state-of-the-art techniques for cloud computing in medical imaging, healthcare technologies, and services. The book focuses on Machine-learning algorithms for health data security Fog computing in IoT-based health care Medical imaging and healthcare applications using fog IoT networks Diagnostic imaging and associated services Image steganography for medical informatics This book aims to help advance scientific research within the broad field of cloud computing in medical imaging, healthcare technologies, and services. It focuses on major trends and challenges in this area and presents work aimed to identify new techniques and their use in biomedical analysis.

Stochastic Modeling for Medical Image Analysis provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis. Today, image-guided computer-assisted diagnostics (CAD) faces two basic challenging problems. The first is the computationally feasible and accurate modeling of images from different modalities to obtain clinically useful information. The second is the accurate and fast inferring of meaningful and clinically valid CAD decisions and/or predictions on the basis of model-guided image analysis. To help address this, this book details original stochastic appearance and shape models with computationally feasible and efficient learning techniques for improving the performance of object detection, segmentation, alignment, and analysis in a number of important CAD applications. The book demonstrates accurate descriptions of visual appearances and shapes of the goal objects and their background to help solve a number of important and challenging CAD problems. The models focus on the first-order marginals of pixel/voxel-wise signals and second- or higher-order Markov-Gibbs random fields of these signals and/or labels of regions supporting the goal objects in the lattice. This valuable resource presents the latest state of the art in stochastic modeling for medical image analysis while incorporating fully tested experimental results throughout.

Provides a comprehensive overview of machine learning and deep learning techniques for biomedical imaging Includes thoracic imaging, abdominal imaging, brain imaging, and retinal imaging Covers new and emerging methods in machine learning Features contributions from leading experts Presents tools to improve computer aided diagnosis

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

As one of the most important tasks in biomedical imaging, image segmentation provides the foundation for quantitative reasoning and diagnostic techniques. A large variety of different imaging techniques, each with its own physical principle and characteristics (e.g., noise modeling), often requires modality-specific algorithmic treatment. In recent years, substantial progress has been made to biomedical image segmentation. Biomedical image segmentation is characterized by several specific factors. This book presents an overview of the advanced segmentation algorithms and their applications.

This book focuses on image based security techniques, namely visual cryptography, watermarking, and steganography. This book is divided into four sections. The first section explores basic to advanced concepts of visual cryptography. The second section of the book covers digital image watermarking including watermarking algorithms, frameworks for modeling watermarking systems, and the evaluation of watermarking techniques. The next section analyzes steganography and steganalysis, including the notion, terminology and building blocks of steganographic communication. The final section of the book describes the concept of hybrid approaches which includes all image-based security techniques. One can also explore various advanced research domains related to the multimedia security field in the final section. The book includes many examples and applications, as well as implementation using MATLAB, wherever required. Features: Provides a comprehensive introduction to visual cryptography, digital watermarking and steganography in one book Includes real-life examples and applications throughout Covers theoretical and practical concepts related to security of other multimedia objects using image based security techniques Presents the implementation of all important concepts in MATLAB

Digital fundus images can effectively diagnose glaucoma and diabetes retinopathy, while infrared imaging can show changes in the vascular tissues. Likening the eye to the conventional camera, Image Analysis and Modeling in Ophthalmology explores the application of advanced image processing in ocular imaging. This book considers how images can be used to effectively diagnose ophthalmologic problems. It introduces multi-modality image processing algorithms as a means for analyzing subtle changes in the eye. It details eye imaging, textural imaging, and modeling, and highlights specific imaging and modeling techniques. The book covers the detection of diabetes retinopathy, glaucoma, anterior segment eye abnormalities, instruments on detection of glaucoma, and development of human eye models using computational fluid dynamics and heat transfer principles to predict inner temperatures of the eye from its surface temperature. It presents an ultrasound biomicroscopy (UBM) system for anterior chamber angle imaging and proposes an automated anterior segment eye disease classification system that can be used for early disease diagnosis and treatment management. It focuses on the segmentation of the blood vessels in high-resolution retinal images and describes the integration of the image processing methodologies in a web-based framework aimed at retinal analysis. The authors introduce the A-Levelset algorithm, explore the ARGALI system to calculate the cup-to-disc ratio (CDR), and describe the Singapore Eye Vessel Assessment (SIVA) system, a holistic tool which brings together various technologies from image processing and artificial intelligence to construct vascular models from retinal images. The text furnishes the working principles of mechanical and optical instruments for the diagnosis and healthcare administration of glaucoma, reviews state-of-the-art CDR calculation detail, and discusses the existing methods and databases. Image Analysis and Modeling in Ophthalmology includes the latest research development in the field of eye modeling and the multi-modality image processing techniques in ocular imaging. It addresses the differences, performance measures, advantages and disadvantages of various approaches, and provides extensive reviews on related fields.

This comprehensive reference work details the latest developments in fluorescence imaging and related biological quantification. It explores the most recent techniques in this imaging technology through the utilization and incorporation of quantification analysis which makes this book unique. It also covers super resolution microscopy with the introduction of 3D imaging and high resolution fluorescence. Many of the chapter authors are world class experts in this medical imaging technology.

This book covers state-of-the-art medical image analysis approaches currently pursued in autism research. Chapters cover recent advances in diagnosis using structural neuroimaging. All aspects of imaging are included, such as electrophysiology (EEG, ERP, QEEG, and MEG), postmortem techniques, and advantages and difficulties of depositing/acquiring images in larger databases. The book incorporates 2D, 3D, and 4D imaging and advances scientific research within the broad field of autism imaging.

This book covers the state-of-the-art approaches for automated non-invasive systems for early cardiovascular disease diagnosis. It includes several prominent imaging modalities such as MRI, CT, and PET technologies. There is a special emphasis placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. Novel 4D based approach is a unique characteristic of this product. This is a comprehensive multi-contributed reference work that will detail the latest developments in spatial, temporal, and functional cardiac imaging. The main aim of this book is to help advance scientific research within the broad field of early detection of cardiovascular disease. This book focuses on major trends and challenges in this area, and it presents work aimed to identify new techniques and their use in biomedical image analysis. Key Features: Includes state-of-the art 4D cardiac image analysis Explores the aspect of automated segmentation of cardiac CT and MR images utilizing both 3D and 4D techniques Provides a novel procedure for improving full-cardiac strain estimation in 3D image appearance characteristics Includes extensive references at the end of each chapter to enhance further study

Lung cancer remains the leading cause of cancer-related deaths worldwide. Early diagnosis can improve the effectiveness of treatment and increase a patient's chances of survival. Thus, there is an urgent need for new technology to diagnose small, malignant lung nodules early as well as large nodules located away from large diameter airways because the current technology-namely, needle biopsy and bronchoscopy-fail to diagnose those cases. However, the analysis of small, indeterminate lung masses is fraught with many technical difficulties. Often patients must be followed for years with serial CT scans in order to establish a diagnosis, but inter-scan variability, slice selection artifacts, differences in degree of inspiration, and scan angles can make comparing serial scans unreliable. Lung Imaging and Computer Aided Diagnosis brings together researchers in pulmonary image analysis to present state-of-the-art image processing techniques for detecting and diagnosing lung cancer at an early stage. The book addresses variables and discrepancies in scans and proposes ways of evaluating small lung masses more consistently to allow for more accurate measurement of growth rates and analysis of shape and appearance of the detected lung nodules. Dealing with all aspects of image analysis of the data, this book examines: Lung segmentation Nodule segmentation Vessels segmentation Airways segmentation Lung registration Detection of lung nodules Diagnosis of detected lung nodules Shape and appearance analysis of lung nodules Contributors also explore the effective use of these methodologies for diagnosis and therapy in clinical applications. Arguably the first book of its kind to address and evaluate image-based diagnostic approaches for the early diagnosis of lung cancer, Lung Imaging and Computer Aided Diagnosis constitutes a valuable resource for biomedical engineers, researchers, and clinicians in lung disease imaging.

Developments in CT technology during the last 20 years have impressively improved its diagnostic potentialities. Part of a two-volume set that covers all aspects of CT imaging, Multi-Detector CT Imaging: Abdomen, Pelvis, and CAD Applications contains easily searchable clinical specialty chapters that provide specific information without need of an index. The coverage goes far beyond just a "how-to" or an encyclopedia of findings, however. The authors have uniformly put techniques, clinical findings, pathologic disease presentations, and clinical implications in practical perspective. It is no wonder that with the critical role CT plays and the rapid innovations in computer technology that advances in the capabilities and complexity of CT imaging continue to evolve. While information about these developments may be scattered about in journals and other resources, this two-volume set provides an authoritative, up-to-date, and educational reference that covets the entire spectrum of CT.

Digital fundus images can effectively diagnose glaucoma and diabetes retinopathy, while infrared imaging can show changes in the vascular tissues. Likening the eye to the conventional camera, Image Analysis and Modeling in Ophthalmology explores the application of advanced image processing in ocular imaging. This book considers how images can be used to effectively diagnose ophthalmologic problems. It introduces multi-modality image processing algorithms as a means for analyzing subtle changes in the eye. It details eye imaging, textural imaging, and modeling, and highlights specific imaging and modeling techniques. The book covers the detection of diabetes retinopathy, glaucoma, anterior segment eye abnormalities, instruments on detection of glaucoma, and development of human eye models using computational fluid dynamics and heat transfer principles to predict inner temperatures of the eye from its surface temperature. It presents an ultrasound biomicroscopy (UBM) system for anterior chamber angle imaging and proposes an automated anterior segment eye disease classification system that can be used for early disease diagnosis and treatment management. It focuses on the segmentation of the blood vessels in high-resolution retinal images and describes the integration of the image processing methodologies in a web-based framework aimed at retinal analysis. The authors introduce the A-Levelset algorithm, explore the ARGALI system to calculate the cup-to-disc ratio (CDR), and describe the Singapore Eye Vessel Assessment (SIVA) system, a holistic tool which brings together various technologies from image processing and artificial intelligence to construct vascular models from retinal images. The text furnishes the working principles of mechanical and optical instruments for the diagnosis and healthcare administration of glaucoma, reviews state-of-the-art CDR calculation detail, and discusses the existing methods and databases. Image Analysis and Modeling in Ophthalmology includes the latest research development in the field of eye modeling and the multi-modality image processing techniques in ocular imaging. It addresses the differences, performance measures, advantages and disadvantages of various approaches, and provides extensive reviews on related fields.

This comprehensive reference work details the latest developments in fluorescence imaging and related biological quantification. It explores the most recent techniques in this imaging technology through the utilization and incorporation of quantification analysis which makes this book unique. It also covers super resolution microscopy with the introduction of 3D imaging and high resolution fluorescence. Many of the chapter authors are world class experts in this medical imaging technology.

Edited by and featuring contributions from world-class researchers, Ophthalmological Imaging and Applications offers a unified work of the latest human eye imaging and modeling techniques that have been proposed and applied to the diagnosis of ophthalmologic problems, including inflammation, cataracts, diabetic retinopathy, and glaucoma. With a focus on theory, basic principles, and results derived from research, the book: Explores various morphological, textural, higher-order spectral, and wavelet transformation techniques used to extract salient features from images of the human eye Examines 2D and 3D finite element and boundary element models of the human eye developed to simulate thermal steady-state conditions Addresses the difficult task of benchmarking the validity of human eye imaging techniques and computer-simulated results with experimental measurements Intended to be a companion to Image Analysis and Modeling in Ophthalmology, this volume covers several aspects of multimodal ophthalmological imaging and applications, presenting information in an accessible manner to appeal to a wide audience of students, researchers, and practitioners. Ophthalmological Imaging and Applications considers promising simulations that pave the way for new possibilities in computational methods for eye health care.

Developments in CT technology during the last 20 years have impressively improved its diagnostic potentialities. Part of a two-volume set that covers all aspects of CT imaging, Multi-Detector CT Imaging: Abdomen, Pelvis, and CAD Applications contains easily searchable clinical specialty chapters that provide specific information without need of an index. The coverage goes far beyond just a "how-to" or an encyclopedia of findings, however. The authors have uniformly put techniques, clinical findings, pathologic disease presentations, and clinical implications in practical perspective. It is no wonder that with the critical role CT plays and the rapid innovations in computer technology that advances in the capabilities and complexity of CT imaging continue to evolve. While information about these developments may be scattered about in journals and other resources, this two-volume set provides an authoritative, up-to-date, and educational reference that covets the entire spectrum of CT.

Developments in CT technology during the last 20 years have impressively improved its diagnostic potentialities. Part of a two-volume set that covers all aspects of CT imaging, Multi-Detector CT Imaging: Principles, Head, Neck, and Vascular Systems contains easily searchable clinical specialty chapters that provide specific information without need of an index. The coverage goes far beyond just a "how-to" or an encyclopedia of findings, however. The authors have uniformly put techniques, clinical findings, pathologic disease presentations, and clinical implications of imaging findings in practical perspective. This Volume Features: Discusses technical principles, CT perfusion, contrast media, postprocessing and clinical applications, and radiation dose Covers imaging of the neck and brain, including paranasal sinuses and the ear, brain tumors, neurodegenerative diseases, CNS infection, and head trauma Examines how to use CT data for planning cardiovascular procedures Includes coverage of pathologies of the pericardium and CT venography Presents information on clinical applications in lung disease With the critical role CT plays and the rapid innovations in computer technology, it is no wonder that advances in the capabilities and complexity of CT imaging continue to evolve. While information about these developments may be scattered about in journals and other resources, this two-volume set provides an authoritative, up-to-date, and educational reference that covets the entire spectrum of CT.

Advanced image processing and mathematical modeling techniques are increasingly being used for the early diagnosis of eye diseases. A comprehensive review of the field, Human Eye Imaging and Modeling details the latest advances and analytical techniques in ocular imaging and modeling. The first part of the book looks at imaging of the fundus as well as infrared imaging. It begins by exploring developments in the analysis of fundus images, particularly for the diagnosis of diabetic retinopathy and glaucoma. It also reviews anterior segment imaging and reports on developments in ocular thermography, especially the use of thermal imaging as the basis of tear evaporimetry and dry eye diagnosis. The second part of the book delves into mathematical modeling of the human eye. Coverage includes modeling of the eye during retinal laser surgery, a framework for optical simulation, heat distribution using a 3D web-splines solution, and exposure to laser radiation. The text also examines computer simulation of the human eye based on principles of heat transfer, as well as various bioheat equations to predict interior temperatures based on the surface temperature. Featuring contributions by established experts in eye imaging, this is a valuable reference for medical personnel and researchers who want to know more about state-of-the-art computer-based imaging and detection methods. It presents novel imaging and modeling algorithms that can aid in early diagnosis, with the aim of enriching the lives of people suffering from eye abnormalities.

Lung cancer remains the leading cause of cancer-related deaths worldwide. Early diagnosis can improve the effectiveness of treatment and increase a patient's chances of survival. Thus, there is an urgent need for new technology to diagnose small, malignant lung nodules early as well as large nodules located away from large diameter airways because the current technology-namely, needle biopsy and bronchoscopy-fail to diagnose those cases. However, the analysis of small, indeterminate lung masses is fraught with many technical difficulties. Often patients must be followed for years with serial CT scans in order to establish a diagnosis, but inter-scan variability, slice selection artifacts, differences in degree of inspiration, and scan angles can make comparing serial scans unreliable. Lung Imaging and Computer Aided Diagnosis brings together researchers in pulmonary image analysis to present state-of-the-art image processing techniques for detecting and diagnosing lung cancer at an early stage. The book addresses variables and discrepancies in scans and proposes ways of evaluating small lung masses more consistently to allow for more accurate measurement of growth rates and analysis of shape and appearance of the detected lung nodules. Dealing with all aspects of image analysis of the data, this book examines: Lung segmentation Nodule segmentation Vessels segmentation Airways segmentation Lung registration Detection of lung nodules Diagnosis of detected lung nodules Shape and appearance analysis of lung nodules Contributors also explore the effective use of these methodologies for diagnosis and therapy in clinical applications. Arguably the first book of its kind to address and evaluate image-based diagnostic approaches for the early diagnosis of lung cancer, Lung Imaging and Computer Aided Diagnosis constitutes a valuable resource for biomedical engineers, researchers, and clinicians in lung disease imaging.

State of the Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. The book provides over views and case studies of advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing, and suitable data analytics useful for clinical diagnosis and research applications. The application of neural network, artificial intelligence and machine learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume One: Neural Networks in Oncology Imaging covers lung cancer, prostate cancer, and bladder cancer. Volume Two: Neural Networks in Brain Disorders and Other Diseases covers autism spectrum disorder, Alzheimer's disease, attention deficit hyperactivity disorder, hypertension, and other diseases. Written by experienced engineers in the field, these two volumes will help engineers, computer scientists, researchers, and clinicians understand the technology and applications of artificial neural networks.

With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.

Diagnostic and Therapeutic Ultrasound has recently taken an explosive growth for better safer, economic, mobile and high quality healthcare. This technology is very appealing for medical applications because it is non-ionizing, non-invasive and it is available in most of the medical and clinical facilities. Its low cost, when compared with other medical image modalities, makes it one of the preferred tools for medical monitoring, follow-up and diagnosis. Besides the traditional fields of Cardiology and Obstetrics, where it is extensively used for long time, it has became also very useful in the diagnosis of diseases of the prostate, liver and coronaries and carotids atherosclerosis. However, Ultrasound images present poor quality, very low signal to noise ratio and a lot of artifacts. The extraction of useful information from Ultrasound data for diagnosis is a challenge task that makes this medical image modality a very active field of research. The difficulties are being overcome and novel and advanced methods are being proposed for detection, characterization and segmentation of abnormalities in several organs. In fact, Ultrasound application range is vast, covering almost all organs of the human body, including the brain where Tran-cranial Doppler Ultrasound is very important to assess the brain vasculature. This book presents some of the recent advances in Ultrasound imaging technology covering several organs and techniques in a Biomedical Engineering (BME) perspective. The focus of the book is in the algorithms, methodologies and systems developed by multidisciplinary research teams of engineers and physicians for Computer-Aided Diagnosis (CAD) purposes. Cardiovascular and Cancer, the most common life-threatening diseases in western countries, are two of the most important topics focused in the book. However, other advanced issues are also presented such as Intravascular Ultrasound, 3D US and Ultrasound in Computer-Aided Surgery (CAS). Some chapters are direct contributions from medical research groups where Ultrasound has also received great attention in the last decade. By this, new techniques based on Ultrasound were introduced in the clinical practice for diagnosis and therapeutics, mainly in hospital facilities.

Atherosclerosis is a degenerative process affecting blood vessels, which determines narrowing of the lumen, plaque growth, and hardening of the walls. It is a risk factor for cardiovascular diseases. The focus of this book is on the management of the atherosclerotic disease. The coverage of this book spans from histological presentation of the various stages of atherosclerotic lesions to the earliest studies in atherosclerosis therapy, from advanced clinical diagnosis to monitoring, follow-up, and home-care of the atherosclerotic patient. The book shows well-established diagnostic techniques covering several medical imaging modalities such as Ultrasounds, IVUS, MRI, Computer Tomography, along with new trends in early and advanced atherosclerosis diagnosis (innovative drugs and tissue characterization procedures). Surgical standards will be presented along with innovative experimental trials for the treatment of the atherosclerotic patient. The book will also cover emerging techniques based on molecular imaging and vibro-acoustics.

Neural Engineering for Autism Spectrum Disorder, Volume Two: Diagnosis and Clinical Analysis presents the latest advances in neural engineering and biomedical engineering as applied to the clinical diagnosis and treatment of Autism Spectrum Disorder (ASD). Advances in the role of neuroimaging, magnetic resonance spectroscopy, MRI, fMRI, DTI, video analysis of sensory-motor and social behaviors, and suitable data analytics useful for clinical diagnosis and research applications for Autism Spectrum Disorder are covered, including relevant case studies. The application of brain signal evaluation, EEG analytics, fuzzy model and temporal fractal analysis of rest state BOLD signals and brain signals are also presented. A clinical guide for general practitioners is provided along with a variety of assessment techniques such as magnetic resonance spectroscopy. The book is presented in two volumes, including Volume One: Imaging and Signal Analysis Techniques comprised of two Parts: Autism and Medical Imaging, and Autism and Signal Analysis. Volume Two: Diagnosis and Treatment includes Autism and Clinical Analysis: Diagnosis, and Autism and Clinical Analysis: Treatment.