"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.

chapter 1|18 pages


part A|125 pages

The receiver operating characteristic (ROC) paradigm

chapter 2|12 pages

The binary paradigm

chapter 3|26 pages

Modeling the binary task

chapter 4|22 pages

The ratings paradigm

chapter 5|10 pages

Empirical AUC

chapter 6|30 pages

Binormal model

chapter 7|23 pages

Sources of variability in AUC

part B|111 pages

Two significance testing methods for the ROC paradigm

chapter 8|16 pages

Hypothesis testing

chapter 11|25 pages

Sample size estimation

part C|197 pages

The free-response ROC (FROC) paradigm

chapter 12|19 pages

The FROC paradigm

chapter 15|14 pages

Visual search paradigms

chapter 16|8 pages

The radiological search model (RSM)

chapter 17|48 pages

Predictions of the RSM

chapter 18|15 pages

Analyzing FROC data

part D|79 pages

Selected advanced topics

chapter 20|28 pages

Proper ROC models

chapter 21|19 pages

The bivariate binormal model

chapter 23|15 pages

Validating CAD analysis