Observer Performance Methods for Diagnostic Imaging: Foundations, Modeling, and Applications with R-Based Examples

Dev P. Chakraborty

December 21, 2017 by CRC Press
Reference - 542 Pages - 95 B/W Illustrations
ISBN 9781482214840 - CAT# K21778
Series: Imaging in Medical Diagnosis and Therapy

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  • Gives an accessible overview of R programming for medical imaging and the methods of observer performance testing.
  • Explains the fundamental statistical concepts.
  • Reinforces learning using worked problems and R software code, in addition to examples that utilize standalone ROC software.
  • Starts with basic ROC analysis and builds to extensions of ROC methods for solving more complex but clinically realistic tasks.
  • Emphasizes psychophysical models of observer performance (e.g., binormal model, contaminated binormal model, proper ROC model), and demonstrates how they can give better results than from purely statistical approaches.
  • Supplementary tools and materials available at:;



"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.


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