Dev P. Chakraborty received his PhD in physics in 1977 from the University of Rochester, NY. Following postdoctoral fellowships at the University of Pennsylvania (UPENN) and the University of Alabama at Birmingham (UAB), since 1982 he has worked as a clinical diagnostic imaging physicist. He is American Board of Radiology certified in Diagnostic Radiological Physics and Medical Nuclear Physics (1987). He has held faculty positions at UAB (1982 - 1988), UPENN (1988-2002) and the University of Pittsburgh (2002-2016). At UPENN he supervised hospital imaging equipment quality control, resident physics instruction and conducted independent research. He is an author on 78 peer-reviewed publications, the majority of which are first-authored. He has received research funding from the Whittaker Foundation, the Office of Women's Health, the FDA, the DOD, and has served as principal investigator on several NIH RO1 grants.

"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."— from the Foreword by Gary T. Barnes, Professor Emeritus, Department of Radiology, University of Alabama Birmingham"As opposed to most of the books with a primary statistical orientation, 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." —Harold L. Kundel, Emeritus Professor, Department of Radiology, Perelman School of Medicine, University of Pennsylvania

• 1 PreliminariesPART A The receiver operating characteristic (ROC) paradigm2 The binary paradigm3 Modeling the binary task4 The ratings paradigm5 Empirical AUC6 Binormal model7 Sources of variability in AUCPART B Two significance testing methods for the ROC paradigm8 Hypothesis testing9 Dorfman–Berbaum–Metz–Hillis (DBMH) analysis10 Obuchowski–Rockette–Hillis (ORH) analysis11 Sample size estimationPART C The free-response ROC (FROC) paradigm12 The FROC paradigm13 Empirical operating characteristics possible with FROC data14 Computation and meanings of empirical FROC FOM-statistics and AUC measures15 Visual search paradigms16 The radiological search model (RSM)17 Predictions of the RSM18 Analyzing FROC data19 Fitting RSM to FROC/ROC data and key findingsPART D Selected advanced topics20 Proper ROC models21 The bivariate binormal model22 Evaluating standalone CAD versus radiologists23 Validating CAD analysis

Dev P. Chakraborty received his PhD in physics in 1977 from the University of Rochester, NY. Following postdoctoral fellowships at the University of Pennsylvania (UPENN) and the University of Alabama at Birmingham (UAB), since 1982 he has worked as a clinical diagnostic imaging physicist. He is American Board of Radiology certified in Diagnostic Radiological Physics and Medical Nuclear Physics (1987). He has held faculty positions at UAB (1982 - 1988), UPENN (1988-2002) and the University of Pittsburgh (2002-2016). At UPENN he supervised hospital imaging equipment quality control, resident physics instruction and conducted independent research. He is an author on 78 peer-reviewed publications, the majority of which are first-authored. He has received research funding from the Whittaker Foundation, the Office of Women's Health, the FDA, the DOD, and has served as principal investigator on several NIH RO1 grants.

"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."— from the Foreword by Gary T. Barnes, Professor Emeritus, Department of Radiology, University of Alabama Birmingham"As opposed to most of the books with a primary statistical orientation, 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." —Harold L. Kundel, Emeritus Professor, Department of Radiology, Perelman School of Medicine, University of Pennsylvania

• 1 PreliminariesPART A The receiver operating characteristic (ROC) paradigm2 The binary paradigm3 Modeling the binary task4 The ratings paradigm5 Empirical AUC6 Binormal model7 Sources of variability in AUCPART B Two significance testing methods for the ROC paradigm8 Hypothesis testing9 Dorfman–Berbaum–Metz–Hillis (DBMH) analysis10 Obuchowski–Rockette–Hillis (ORH) analysis11 Sample size estimationPART C The free-response ROC (FROC) paradigm12 The FROC paradigm13 Empirical operating characteristics possible with FROC data14 Computation and meanings of empirical FROC FOM-statistics and AUC measures15 Visual search paradigms16 The radiological search model (RSM)17 Predictions of the RSM18 Analyzing FROC data19 Fitting RSM to FROC/ROC data and key findingsPART D Selected advanced topics20 Proper ROC models21 The bivariate binormal model22 Evaluating standalone CAD versus radiologists23 Validating CAD analysis

Dev P. Chakraborty received his PhD in physics in 1977 from the University of Rochester, NY. Following postdoctoral fellowships at the University of Pennsylvania (UPENN) and the University of Alabama at Birmingham (UAB), since 1982 he has worked as a clinical diagnostic imaging physicist. He is American Board of Radiology certified in Diagnostic Radiological Physics and Medical Nuclear Physics (1987). He has held faculty positions at UAB (1982 - 1988), UPENN (1988-2002) and the University of Pittsburgh (2002-2016). At UPENN he supervised hospital imaging equipment quality control, resident physics instruction and conducted independent research. He is an author on 78 peer-reviewed publications, the majority of which are first-authored. He has received research funding from the Whittaker Foundation, the Office of Women's Health, the FDA, the DOD, and has served as principal investigator on several NIH RO1 grants.

"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."— from the Foreword by Gary T. Barnes, Professor Emeritus, Department of Radiology, University of Alabama Birmingham"As opposed to most of the books with a primary statistical orientation, 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." —Harold L. Kundel, Emeritus Professor, Department of Radiology, Perelman School of Medicine, University of Pennsylvania

• 1 PreliminariesPART A The receiver operating characteristic (ROC) paradigm2 The binary paradigm3 Modeling the binary task4 The ratings paradigm5 Empirical AUC6 Binormal model7 Sources of variability in AUCPART B Two significance testing methods for the ROC paradigm8 Hypothesis testing9 Dorfman–Berbaum–Metz–Hillis (DBMH) analysis10 Obuchowski–Rockette–Hillis (ORH) analysis11 Sample size estimationPART C The free-response ROC (FROC) paradigm12 The FROC paradigm13 Empirical operating characteristics possible with FROC data14 Computation and meanings of empirical FROC FOM-statistics and AUC measures15 Visual search paradigms16 The radiological search model (RSM)17 Predictions of the RSM18 Analyzing FROC data19 Fitting RSM to FROC/ROC data and key findingsPART D Selected advanced topics20 Proper ROC models21 The bivariate binormal model22 Evaluating standalone CAD versus radiologists23 Validating CAD analysis