The book covers novel strategies of state of the art in engineering and clinical analysis and approaches for analyzing abdominal imaging, including lung, mediastinum, pleura, liver, kidney and gallbladder. In the last years the imaging techniques have experienced a tremendous improvement in the diagnosis and characterization of the pathologies that affect abdominal organs. In particular, the introduction of extremely fast CT scanners and high Magnetic field MR Systems allow imaging with an exquisite level of detail the anatomy and pathology of liver, kidney, pancreas, gallbladder as well as lung and mediastinum. Moreover, thanks to the development of powerful computer hardware and advanced mathematical algorithms the quantitative and automated\semi automated diagnosis of the pathology is becoming a reality. Medical image analysis plays an essential role in the medical imaging field, including computer-aided diagnosis, organ/lesion segmentation, image registration, and image-guided therapy. This book will cover all the imaging techniques, potential for applying such imaging clinically, and offer present and future applications as applied to the abdomen and thoracic imaging with the most world renowned scientists in these fields. The main aim of this book is to help advance scientific research within the broad field of abdominal imaging. This book focuses on major trends and challenges in this area, and it presents work aimed to identify new techniques and their use in medical imaging analysis for abdominal imaging.

Data compiled by the Center for Disease Control and Prevention indicates an alarming and continuing increase in the prevalence of autism. Despite intensive research during the last few decades, autism remains a behavioral defined syndrome wherein diagnostic criteria lack in construct validity. And, contrary to other conditions like diabetes and hypertension, there are no biomarkers for autism. However, new imaging methods are changing the way we think about autism, bringing us closer to a falsifiable definition for the condition, identifying affected individuals earlier in life, and recognizing different subtypes of autism. The imaging modalities discussed in this book emphasize the power of new technology to uncover important clues about the condition with the hope of developing effective interventions. Imaging the Brain in Autism was created to examine autism from a unique perspective that would emphasize results from different imaging technologies. These techniques show brain abnormalities in a significant percentage of patients, abnormalities that translate into aberrant functioning and significant clinical symptomatology. It is our hope that this newfound understanding will make the field work collaborative and provide a path that minimizes technical impediments.

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.

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.

Cardiovascular and Coronary Artery Imaging, Volume One covers state-of-the-art approaches for automated non-invasive systems in early cardiovascular disease diagnosis. The book includes several prominent imaging modalities, such as MRI, CT and PET technologies. A special emphasis is placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. This is a comprehensive, multi-contributed reference work that details the latest developments in spatial, temporal and functional cardiac imaging.

Cardiovascular and Coronary Artery Imaging, Volume Two presents the basics of echocardiography, nuclear imaging and magnetic resonance imaging (MRI) and provides insights into their appropriate use. The book covers state-of-the-art approaches for automated non-invasive systems for early cardiovascular and coronary artery disease diagnosis. It includes several prominent imaging modalities such as MRI, CT and PET technologies. Other sections focus on major trends and challenges in this area and present the latest techniques for cardiovascular and coronary image analysis.

State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. 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 are covered, including relevant case studies. 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 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.

Neural Engineering for Autism Spectrum Disorder, Volume One: Imaging and Signal Analysis Techniques 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, infrared spectroscopy, sMRI, fMRI, DTI, 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, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals are presented for detection and estimation of the degree of ASD.

Diabetes and Fundus OCT brings together a stellar cast of authors who review the computer-aided diagnostic (CAD) systems developed to diagnose non-proliferative diabetic retinopathy in an automated fashion using Fundus and OCTA images. Academic researchers, bioengineers, new investigators and students interested in diabetes and retinopathy need an authoritative reference to bring this multidisciplinary field together to help reduce the amount of time spent on source-searching and instead focus on actual research and the clinical application. This reference depicts the current clinical understanding of diabetic retinopathy, along with the many scientific advances in understanding this condition. As the role of optical coherence tomography (OCT) in the assessment and management of diabetic retinopathy has become significant in understanding the vireo retinal relationships and the internal architecture of the retina, this information is more critical than ever.

Computer-Assisted Diagnosis: Diabetes and Cardiovascular Disease brings together multifaceted information on research and clinical applications from an academic, clinical, bioengineering and bioinformatics perspective. The editors provide a stellar, diverse list of authors to explore this interesting field. Academic researchers, bioengineers, new investigators and students interested in diabetes and heart disease need an authoritative reference to reduce the amount of time spent on source-searching so they can spend more time on actual research and clinical application. This reference accomplishes this with contributions by authors from around the world.

Data compiled by the Center for Disease Control and Prevention indicates an alarming and continuing increase in the prevalence of autism. Despite intensive research during the last few decades, autism remains a behavioral defined syndrome wherein diagnostic criteria lack in construct validity. And, contrary to other conditions like diabetes and hypertension, there are no biomarkers for autism. However, new imaging methods are changing the way we think about autism, bringing us closer to a falsifiable definition for the condition, identifying affected individuals earlier in life, and recognizing different subtypes of autism. The imaging modalities discussed in this book emphasize the power of new technology to uncover important clues about the condition with the hope of developing effective interventions. Imaging the Brain in Autism was created to examine autism from a unique perspective that would emphasize results from different imaging technologies. These techniques show brain abnormalities in a significant percentage of patients, abnormalities that translate into aberrant functioning and significant clinical symptomatology. It is our hope that this newfound understanding will make the field work collaborative and provide a path that minimizes technical impediments.

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.

Diabetes and Retinopathy brings together the multifaceted information about the research and clinical application from academic, clinical, bioengineering and bioinformatics perspectives. The editors bring together a stellar cast of authors to pull together this diverse and interesting field. Academic researchers, bioengineers, new investigators and students interested in diabetes and retinopathy need an authoritative reference to bring this multidisciplinary field together to reduce the amount of time spent on source-searching and more time on actual research and the clinical application. This reference depicts the current clinical understanding of DR as well as the many scientific advances in understanding this condition.