Observer Performance Methods for Diagnostic Imaging Foundations, Modeling, and Applications with R-Based Examples
"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.
PART A The receiver operating characteristic (ROC) paradigm
2 The binary paradigm
3 Modeling the binary task
4 The ratings paradigm
5 Empirical AUC
6 Binormal model
7 Sources of variability in AUC
PART B Two significance testing methods for the ROC paradigm
8 Hypothesis testing
9 Dorfman–Berbaum–Metz–Hillis (DBMH) analysis
10 Obuchowski–Rockette–Hillis (ORH) analysis
11 Sample size estimation
PART C The free-response ROC (FROC) paradigm
12 The FROC paradigm
13 Empirical operating characteristics possible with FROC data
14 Computation and meanings of empirical FROC FOM-statistics and AUC measures
15 Visual search paradigms
16 The radiological search model (RSM)
17 Predictions of the RSM
18 Analyzing FROC data
19 Fitting RSM to FROC/ROC data and key findings
PART D Selected advanced topics
20 Proper ROC models
21 The bivariate binormal model
22 Evaluating standalone CAD versus radiologists
23 Validating CAD analysis