This book constitutes the thoroughly refereed post-workshop proceedings of the First International Workshop on Patch-based Techniques in Medical Images, Patch-MI 2015, which was held in conjunction with MICCAI 2015, in Munich, Germany, in October 2015. The 25 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The topics covered are such as image segmentation of anatomical structures or lesions; image enhancement; computer-aided prognostic and diagnostic; multi-modality fusion; mono and multi modal image synthesis; image retrieval; dynamic, functional physiologic and anatomic imaging; super-pixel/voxel in medical image analysis; sparse dictionary learning and sparse coding; analysis of 2D, 2D+t, 3D, 3D+t, 4D, and 4D+t data.

Medical imaging is a major part of twenty-first century health care. This introduction explores the mathematical aspects of imaging in medicine to explain approximation methods in addition to computer implementation of inversion algorithms.

This book constitutes revised selected papers from the 4th International Workshop on Clinical Image-Based Procedures, CLIP 2015, held in conjunction with MICCAI 2015 in Munich, Germany, in October 2015. The 15 papers presented in this volume were carefully reviewed and selected from 22 submissions. CLIP focuses on translational research; therefore, the goal of the works presented in this workshop is to bring basic research methods closer to the clinical practice. A highlight of this workshop is the subject of strategies for personalized medicine to enhance diagnosis, treatment and interventions.

A practical clinically relevant introduction to diagnostic radiology Introduction to Basic Radiology is written to provide non-radiologists with the level of knowledge necessary to order correct radiological examinations, improve image interpretation, and enhance their interpretation of various radiological manifestations. The book focuses on the clinical scenarios most often encountered in daily practice and discusses practical imaging techniques and protocols used to address common problems. Relevant case scenarios are included to demonstrate how to reach a specific diagnosis. Introduction to Basic Radiology is divided into ten chapters. The first two chapters provide basic information on various diagnostic imaging techniques and control agents. Each of the following chapters discuss imaging of specific organ systems and begin with a description of the imaging modality of choice and illustrates the relevant features to help simplify the differential diagnosis. You will also find important chapters on pediatric radiology and women's imaging. Unlike other introductory texts on the subject, this book treats diagnosis from a practical point of view. Rather than discuss various diseases and classify them from the pathologic standpoint, Introduction to Basic Radiology utilizes cases from the emergency room and physician's offices and uses a practical approach to reach a diagnosis. The cases walk you through a radiology expert’s analysis of imaging patterns. These cases are presented progressively, with the expert's thinking process described in detail. The cases highlight clinical presentation, clinical suspicion, modality of choice, radiologic technique, and pertinent imaging features of common disease processes.

This unique book fills a void in radiology interpretation texts by encompassing the foundational tools and concepts of the full range of medical imaging, including radiology, the basics of interpretation of plain radiographs, comparison with other testing modalities, the rationale for selecting the first diagnostic step, and exploration and interpretation of chest, abdomen, extremity, and spinal radiographs. A concise, easy-to-use reference, it includes written descriptions enhanced with figures, tables, and actual patient films to demonstrate concepts, and discusses--in easily accessible language--differences in testing modalities. The text also features a step-by-step guide to the interpretation of radiographs. This resource describes and compares available diagnostic modalities, including plain radiograph, CT scan, nuclear imaging, MRI, and ultrasound. It discusses pediatric considerations and includes separate chapters for the chest, abdomen, upper and lower extremities, and the cervical, thoracic, and lumbar spine. The book will be an asset to nurse practitioners and physician assistants working in all emergency, urgent, intensive, and primary care settings. It will also benefit medical students and graduate students in acute care, family, adult/gerontology, and emergency nurse practitioner programs, as well as emergency/trauma clinical nurse specialists, and hospitalists and intensivist nurse practitioners. Key Features: Integrates the basics of radiology, CT scans, nuclear imaging, MRIs, and ultrasound, their characteristics and differences among testing modalities, and basic step-by-step interpretation skills Relevant to a wide range of nurse practitioners, physician assistants, and other mid-level providers in multiple settings Includes a step-by-step guide to the interpretation of the radiographs Delivers an easy-to-understand approach to selecting diagnostic imaging tests Presents actual images and figures to demonstrate concepts, which are also available digitally

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