Jasjit S. Suri

Ayman S. El-Baz

"This book focuses on major trends and challenges in the area of dyslexia, epilepsy and Parkinson's, and aims to identify new techniques and their applications in biomedical analysis. This fifth volume on neurological disorders explores topics such as real-time epilepsy prediction applied on EEG pediatric data; delineation of epileptogenic zone; behavioral and biological correlates and treatment of dyslexia; potential biomarkers of Parkinson's disease; Rett Syndrome; and automatic assessment of motor impairments for Parkinson's disease. This is an essential reference for students and researchers in medical imaging, brain imaging, image processing, and neurology." -- Prové de l'editor.

Applications in dyslexia, epilepsy and Parkinson's. Volume 5

Neurological Disorders and Imaging Physics

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. Provides valuable information for academic clinicians, researchers, bioengineers and industry on diabetes and retinopathy Discusses the impact of diabetic retinopathy, a major cause of new-onset visual loss in all the industrialized nations Covers statistical classification techniques and risk stratification

Diabetes and Retinopathy

Manuel F. Casanova

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.

Imaging the Brain in Autism

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. Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of imaging technologies Provides in-depth technical coverage of computer-aided diagnosis (CAD), with coverage of computer-aided classification, Unified Deep Learning Frameworks, mammography, fundus imaging, optical coherence tomography, cryo-electron tomography, 3D MRI, CT, and more. Covers deep learning for several medical conditions including renal, retinal, breast, skin, and dental abnormalities, Medical Image Analysis, as well as detection, segmentation, and classification via AI.

Volume 1

State of the Art in Neural Networks and Their Applications

Luca Saba

Jasjit Suri

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. ​

An Engineering & Clinical Perspective

Abdomen and Thoracic Imaging

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. Includes unique information for academic clinicians, researchers and bioengineers Provides insights needed to understand the imaging modalities involved, the unmet clinical need that is being addressed, and the engineering and technical approaches applied Brings together details on the retinal vasculature in diabetics as imaged by optical coherence tomography angiography and automated detection of retinal disease

Diabetes and Fundus OCT

Lung cancer is one of the most common cancers in both men and women worldwide. Early diagnosis of lung cancer can significantly increase the chances of a patient's survival, yet early detection has historically been difficult. As a result, there has been a great deal of progress in the development of accurate and fast diagnostic tools in recent years. Lung Cancer and Imaging provides an introduction to both the methods currently used in lung cancer diagnosis and the promising new techniques that are emerging. Areas covered include the major trends and challenges in lung cancer detection and diagnosis, classification of cancer types, lung feature extraction in joint PET/CT images, and algorithms in the area of low dosage CT lung cancer images.

Ayman S. El-Baz

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