"This book presents the technology evaluation methodology from the point of view of radiological physics and contrasts the purely physical evaluation of image quality with the determination of diagnostic outcome through the study of observer performance. The reader is taken through the arguments with concrete examples illustrated by code in R, an open source statistical language." – from the Foreword by Prof. Harold L. Kundel, Department of Radiology, Perelman School of Medicine, University of Pennsylvania "This book will benefit individuals interested in observer performance evaluations in diagnostic medical imaging and provide additional insights to those that have worked in the field for many years." – Prof. Gary T. Barnes, Department of Radiology, University of Alabama at Birmingham This book provides a complete introductory overview of this growing field and its applications in medical imaging, utilizing worked examples and exercises to demystify statistics for readers of any background. It includes a tutorial on the use of the open source, widely used R software, as well as basic statistical background, before addressing localization tasks common in medical imaging. The coverage includes a discussion of study design basics and the use of the techniques in imaging system optimization, memory effects in clinical interpretations, predictions of clinical task performance, alternatives to ROC analysis, and non-medical applications. Dev P. Chakraborty, PhD, is a clinical diagnostic imaging physicist, certified by the American Board of Radiology in Diagnostic Radiological Physics and Medical Nuclear Physics. He has held faculty positions at the University of Alabama at Birmingham, University of Pennsylvania, and most recently at the University of Pittsburgh.

A state-of-the-art review of key topics in medical image perception science and practice, including associated techniques, illustrations and examples. This second edition contains extensive updates and substantial new content. Written by key figures in the field, it covers a wide range of topics including signal detection, image interpretation and advanced image analysis (e.g. deep learning) techniques for interpretive and computational perception. It provides an overview of the key techniques of medical image perception and observer performance research, and includes examples and applications across clinical disciplines including radiology, pathology and oncology. A final chapter discusses the future prospects of medical image perception and assesses upcoming challenges and possibilities, enabling readers to identify new areas for research. Written for both newcomers to the field and experienced researchers and clinicians, this book provides a comprehensive reference for those interested in medical image perception as means to advance knowledge and improve human health.

Evaluation of Diagnostic Systems: Methods from Signal Detection Theory addresses the many issues that arise in evaluating the performance of a diagnostic system, across the wide range of settings in which such systems are used. These settings include clinical medicine, industrial quality control, environmental monitoring and investigation, machine and metals inspection, military monitoring, information retrieval, and crime investigation. The book is divided into three parts encompassing 11 chapters that emphasize the interpretation of diagnostic visual images by human observers. The first part of the book describes quantitative methods for measuring the accuracy of a system and the statistical techniques for drawing inferences from performance tests. The subsequent part covers study design and includes a detailed description of the form and conduct of an image-interpretation test. The concluding part examines the case study of a medical imaging system that serves as an example of both simple and complex applications. In this part, three mammographic modalities are used: industrial film radiography, low-dose film radiography, and xeroradiography. The case study focuses on the overall reliability of accuracy indices made by its main components, that is, the variabilities across cases, across readers, and within individual readers. The supplementary texts provide study protocols, a computer program for processing test results, and an extensive list of references that will assist the reader in applying those evaluative methods to diagnostic systems in any setting. This book is of value to scientists and engineers, as well as to applied, quantitative, or experimental psychologists who are engaged in the study of the human processes of discrimination and decision making in either perceptual or cognitive tasks.

A state-of-the-art review of key topics in medical image perception science and practice, including associated techniques, illustrations and examples. This second edition contains extensive updates and substantial new content. Written by key figures in the field, it covers a wide range of topics including signal detection, image interpretation and advanced image analysis (e.g. deep learning) techniques for interpretive and computational perception. It provides an overview of the key techniques of medical image perception and observer performance research, and includes examples and applications across clinical disciplines including radiology, pathology and oncology. A final chapter discusses the future prospects of medical image perception and assesses upcoming challenges and possibilities, enabling readers to identify new areas for research. Written for both newcomers to the field and experienced researchers and clinicians, this book provides a comprehensive reference for those interested in medical image perception as means to advance knowledge and improve human health.