Prof. Eric W. Clarkson Profile

Prof. Eric W. Clarkson

Associate Research Professor at Univ of Arizona

SPIE Involvement:

Author

Publications (45)

Proceedings Article | 3 April 2023 Presentation + Paper

Approximating the idea observer on list mode data with comparison to CNN methods

Dan Li, Eric Clarkson

Proceedings Volume 12467, 124670G (2023) https://doi.org/10.1117/12.2655721

KEYWORDS: Monte Carlo methods, Signal detection, Imaging systems, Machine learning

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Performance of list mode Hotelling observer and comparison to a neural network observer

Dan Li, Eric Clarkson

Proceedings Volume 12035, 1203513 (2022) https://doi.org/10.1117/12.2613068

KEYWORDS: Image quality, Neural networks, Data modeling, Signal detection, Photons, Machine learning, Fourier transforms, Imaging systems, Signal to noise ratio, Signal processing

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Proceedings Article | 21 March 2022 Presentation

Observer performance in multi-technology imaging

Matthew Kupinski, Eric Clarkson, Kelsea Cronin, James Woolfenden, John Humm, Lars Furenlid

Proceedings Volume PC12035, PC1203504 (2022) https://doi.org/10.1117/12.2613085

KEYWORDS: Sensors, Imaging systems, Spatial resolution, Single photon emission computed tomography, Tomography, Positron emission tomography, Nuclear medicine, Heart, Cameras

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SPIE Journal Paper | 22 February 2019

Risk analysis, ideal observers, and receiver operating characteristic curves for tasks that combine detection and estimation

Eric Clarkson

JMI, Vol. 6, Issue 01, 015502, (February 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.015502

KEYWORDS: Receivers, Statistical analysis, Imaging systems, Signal detection, Medical imaging, Analytical research, Error analysis, Tumors, Cancer, Biomedical optics

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Proceedings Article | 13 May 2016 Paper

An automated method for registering lidar data in restrictive, tunnel-like environments

Walter Zacherl, Eustace Dereniak, Lars Furenlid, Eric Clarkson

Proceedings Volume 9832, 98320D (2016) https://doi.org/10.1117/12.2223453

KEYWORDS: LIDAR, Gaussian filters, Spherical lenses, Algorithm development, Detection and tracking algorithms, Sensors, Optical spheres, Panoramic photography, Scanners, Laser scanners

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Proceedings Article | 12 May 2016 Paper

Figures of merit for optimizing imaging systems on joint estimation/detection tasks

Eric Clarkson

Proceedings Volume 9847, 98470S (2016) https://doi.org/10.1117/12.2223502

KEYWORDS: X-rays, Imaging systems, Error analysis, Signal detection, Receivers, X-ray imaging, 3D image processing, Information security, 3D image reconstruction, Lead

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Proceedings Article | 12 May 2016 Paper

Estimation and detection information trade-off for x-ray system optimization

Johnathan Cushing, Sagar Mandava, Eric Clarkson, Ali Bilgin

Proceedings Volume 9847, 98470U (2016) https://doi.org/10.1117/12.2223370

KEYWORDS: Imaging systems, X-rays, Statistical analysis, Multiplexing, Signal to noise ratio, Computing systems, X-ray computed tomography, Associative arrays, Signal detection, Gold

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Proceedings Article | 24 March 2016 Paper

Applying the J-optimal channelized quadratic observer to SPECT myocardial perfusion defect detection

Meredith Kupinski, Eric Clarkson, Michael Ghaly, Eric Frey

Proceedings Volume 9787, 978708 (2016) https://doi.org/10.1117/12.2217846

KEYWORDS: Single photon emission computed tomography, Defect detection, Computed tomography, Statistical analysis, Electronic filtering, Matrices, Optimization (mathematics), Reconstruction algorithms, Image compression, Data modeling, Medical imaging

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Proceedings Article | 27 August 2015 Paper

Negative temporal cross covariance in SrI2:Eu

Harrison Barrett, David Fastje, Eric Clarkson, Lars Furenlid, Kanai Shah, Urmila Shirwadkar, Vaibhav Bora

Proceedings Volume 9595, 959504 (2015) https://doi.org/10.1117/12.2214406

KEYWORDS: Scintillation, Sensors, Gamma radiation, Signal detection, Crystals, Oscilloscopes, Scintillators, Photons, Interference (communication), Europium

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Proceedings Article | 27 August 2015 Paper

Optimization of an adaptive SPECT system with the scanning linear estimator

Nasrin Ghanbari, Eric Clarkson, Matthew Kupinski, Xin Li

Proceedings Volume 9594, 95940A (2015) https://doi.org/10.1117/12.2195782

KEYWORDS: Imaging systems, Single photon emission computed tomography, Monte Carlo methods, Error analysis, Interference (communication), Image quality, Sensors, Photons, Computing systems, Image analysis

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Proceedings Article | 5 March 2015 Paper

Application of maximum-likelihood estimation in optical coherence tomography for nanometer-class thickness estimation

Jinxin Huang, Qun Yuan, Patrice Tankam, Eric Clarkson, Matthew Kupinski, Holly Hindman, James Aquavella, Jannick Rolland

Proceedings Volume 9315, 93150F (2015) https://doi.org/10.1117/12.2083160

KEYWORDS: Optical coherence tomography, Point spread functions, Refractive index, Mirrors, Coating, Eye, Interfaces, Biological research, Statistical analysis, Cameras

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SPIE Journal Paper | 12 November 2014 Open Access

Relating the statistics of the angle of linear polarization to measurement uncertainty of the Stokes vector

Meredith Kupinski, Russell Chipman, Eric Clarkson

OE, Vol. 53, Issue 11, 113108, (November 2014) https://doi.org/10.1117/12.10.1117/1.OE.53.11.113108

KEYWORDS: Polarization, Statistical analysis, Polarimetry, Clouds, Multiple scattering, Error analysis, Scattering, Bessel functions, Optical engineering, Atmospheric modeling

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Proceedings Article | 20 October 2014 Paper

Molecular imaging in the College of Optical Sciences: an overview of two decades of instrumentation development

Lars Furenlid, Harrison Barrett, H. Bradford Barber, Eric Clarkson, Matthew Kupinski, Zhonglin Liu, Gail Stevenson, James Woolfenden

Proceedings Volume 9186, 91860J (2014) https://doi.org/10.1117/12.2064808

KEYWORDS: Imaging systems, Sensors, Cameras, Single photon emission computed tomography, Gamma radiation, Computing systems, Tomography, Collimators, Positron emission tomography, Electronics

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Proceedings Article | 4 March 2014 Paper

Simultaneous measurement of lipid and aqueous layers of tear film using optical coherence tomography and statistical decision theory

Jinxin Huang, Eric Clarkson, Matthew Kupinski, Jannick Rolland

Proceedings Volume 8936, 89360A (2014) https://doi.org/10.1117/12.2041898

KEYWORDS: Optical coherence tomography, Mathematical modeling, Sensors, Computer simulations, Error analysis, Statistical analysis, Interfaces, Eye, Cameras, Point spread functions

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Proceedings Article | 26 September 2013 Paper

Impact of fano factor on position and energy estimation in scintillation detectors

Vaibhav Bora, Harrison Barrett, Abhinav Jha, Eric Clarkson

Proceedings Volume 8854, 885405 (2013) https://doi.org/10.1117/12.2033642

KEYWORDS: Sensors, Photons, Scintillation, Crystals, Scintillators, Gamma radiation, Geometrical optics, Detector arrays, Spatial resolution, Image sensors

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Proceedings Article | 19 March 2013 Paper

Joint reconstruction of activity and attenuation map using LM SPECT emission data

Abhinav Jha, Eric Clarkson, Matthew Kupinski, Harrison Barrett

Proceedings Volume 8668, 86681W (2013) https://doi.org/10.1117/12.2008111

KEYWORDS: Signal attenuation, Single photon emission computed tomography, Expectation maximization algorithms, Reconstruction algorithms, Computed tomography, Data modeling, Gamma radiation, Compton scattering, Algorithm development, Data processing

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Proceedings Article | 13 March 2009 Paper

Singular value decomposition of pinhole SPECT systems

John Aarsvold, Robin Palit, Harrison Barrett, Eric Clarkson, Yariv Grobshtein, Lana Volokh, Matthew Kupinski

Proceedings Volume 7263, 72631U (2009) https://doi.org/10.1117/12.813799

KEYWORDS: Imaging systems, Single photon emission computed tomography, Cameras, Sensors, Computing systems, Matrices, Scintillation, Data modeling, Systems modeling, Radiology

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Proceedings Article | 24 March 2008 Paper

Markov-chain Monte Carlo for the performance of a channelized-ideal observer in detection tasks with non-Gaussian lumpy backgrounds

Subok Park, Eric Clarkson

Proceedings Volume 6917, 69170T (2008) https://doi.org/10.1117/12.771704

KEYWORDS: Monte Carlo methods, Imaging systems, Statistical analysis, Binary data, Signal detection, Medical imaging, Interference (communication), Error analysis, Diagnostics, Receivers

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Proceedings Article | 12 February 2008 Paper

Detection of abnormalities in biological tissue using optical coherence tomography

K. Cheong, J. Rolland, E. Clarkson

Proceedings Volume 6849, 68490L (2008) https://doi.org/10.1117/12.764735

KEYWORDS: Tissues, Scattering, Optical coherence tomography, Tissue optics, Biomedical optics, Bandpass filters, Refractive index, Target detection, Interfaces, Optical filters

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Proceedings Article | 30 March 2007 Paper

Adaptive Hotelling discriminant functions

Arthur Brème, Harrison Barrett, Eric Clarkson, Matthew Kupinski

Proceedings Volume 6515, 65150T (2007) https://doi.org/10.1117/12.707804

KEYWORDS: Signal detection, Tumors, Statistical analysis, Imaging systems, Image filtering, Medical imaging, Tissues, Image analysis, Fourier transforms, Performance modeling

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Proceedings Article | 30 March 2007 Paper

Approximating the test statistic distribution and ALROC in signal-detection tasks with signal location uncertainty

Fangfang Shen, Eric Clarkson

Proceedings Volume 6515, 651510 (2007) https://doi.org/10.1117/12.708666

KEYWORDS: Signal detection, Tolerancing, Imaging systems, Information operations, Medical imaging, Electronic filtering, Matrices, Computing systems, Computer simulations, Statistical analysis

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Proceedings Article | 21 March 2007 Paper

Evaluation of hardware in a small-animal SPECT system using reconstructed images

Donald Wilson, Eric Clarkson, Jacob Hesterman, Matthew Kupinski, Harrison Barrett

Proceedings Volume 6515, 65151G (2007) https://doi.org/10.1117/12.709730

KEYWORDS: Imaging systems, Single photon emission computed tomography, Objectives, Signal detection, Image quality, Matrices, Multiplexing, Cameras, Quantitative analysis, Tomography

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Proceedings Article | 20 March 2007 Paper

Bias in Hotelling observer performance computed from finite data

Matthew Kupinski, Eric Clarkson, Jacob Hesterman

Proceedings Volume 6515, 65150S (2007) https://doi.org/10.1117/12.707800

KEYWORDS: Signal to noise ratio, Statistical analysis, Sensors, Imaging systems, Image analysis, Matrices, Medical imaging, Solids, Tumors, Image quality

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Proceedings Article | 8 March 2007 Paper

Estimation ROC curves and their corresponding ideal observers

Eric Clarkson

Proceedings Volume 6515, 651504 (2007) https://doi.org/10.1117/12.708250

KEYWORDS: Signal detection, Statistical analysis, Image analysis, Binary data, Medical imaging, Tolerancing, Radio optics, Radiology, Image quality, Imaging systems

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Proceedings Article | 17 March 2006 Paper

Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds

Eric Clarkson, Harrison Barrett, Kyle Myers, Matthew Kupinski, Subok Park

Proceedings Volume 6146, 61460P (2006) https://doi.org/10.1117/12.653931

KEYWORDS: Signal detection, Monte Carlo methods, Medical imaging, Image quality, Imaging systems, Computer simulations, Statistical analysis, Signal to noise ratio, Quality measurement, Image classification

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Proceedings Article | 6 April 2005 Paper

Probabilistic foundations of the MRMC method

Matthew Kupinski, Harrison Barrett, Eric Clarkson

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.595685

KEYWORDS: Stochastic processes, Probability theory, Statistical analysis, Statistical modeling, Medical imaging, Radiology, Analytical research, Process modeling, Radio optics, Gamma ray imaging

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Current approaches to ROC analysis use the MRMC (multiple-reader, multiple-case) paradigm in which several readers read each case and their ratings are used to construct an estimate of the area under the ROC curve or some other ROC-related parameter. Standard practice is to decompose the parameter of interest according to a linear model into terms that depend in various ways on the readers, cases and modalities. It is assumed that the terms are statistically independent (or at least uncorrelated). Bootstrap methods are then used to estimate the variance of the estimate and the contributions from the individual terms in the assumed expansion. Though the methodological aspects of MRMC analysis have been studied in detail, the literature on the probabilistic basis of the individual terms is sparse. In particular, few papers state what probability law applies to each term and what underlying assumptions are needed for the assumed independence. This paper approaches the MRMC problem from a mechanistic perspective. For a single modality, three sources of randomness are included: the images, the reader skill and the reader uncertainty. The probability law on the parameter estimate is written in terms of three nested conditional probabilities, and random variables associated with this probability are referred to as triply stochastic. The triply stochastic probability is used to define the overall average of any ROC parameter as well as certain partial averages of utility in MRMC analysis. When this theory is applied to estimates of an ROC parameter for a single modality, it is shown that the variance of the estimate can be written as a sum of three terms, rather than the four that would be expected in MRMC analysis. The usual terms in MRMC expansions do not appear naturally in multiply-stochastic theory. A rigorous MRMC expansion can be constructed by adding and subtracting partial averages to the parameter of interest in a tautological manner. In this approach the parameter is decomposed into a sum of four random uncorrelated, zero-mean random variables, with each term clearly defined in terms of conditional probabilities. When the variance of the expansion is computed, however, numerous subtractions occur, and there is no apparent advantage to computing the variance term by term; the final result is the same as one gets from the triply stochastic decomposition, at least for the Wilcoxon estimator. No other nontrivial MRMC expansion appears to be possible.

Proceedings Article | 6 April 2005 Paper

Extending the channelized Hotelling observer to account for signal uncertainty and estimation tasks

Matthew Kupinski, Eric Clarkson

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.595680

KEYWORDS: Signal detection, Imaging systems, Image quality, Quality measurement, Signal to noise ratio, Medical imaging, Image processing, Associative arrays, Statistical analysis, Channel projecting optics

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Proceedings Article | 6 April 2005 Paper

Efficiency of human and model observers for signal-detection tasks in non-Gaussian distributed lumpy backgrounds

Eric Clarkson, Harrison Barrett, Matthew Kupinski, Subok Park

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.592238

KEYWORDS: Signal detection, Imaging systems, Image processing, Statistical analysis, Systems modeling, Signal to noise ratio, Monte Carlo methods, Image quality, Medical imaging, Mathematical modeling

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SPIE Journal Paper | 1 July 2004

Model of wide-angle optical field propagation using scalar diffraction theory

Vesselin Shaoulov, Ravi Satya, Guy Schiavone, Eric Clarkson, Jannick Rolland

OE, Vol. 43, Issue 07, (July 2004) https://doi.org/10.1117/12.10.1117/1.1751401

KEYWORDS: Geometrical optics, Imaging systems, Image processing, Diffraction, Systems modeling, Optical aberrations, Wavefronts, Laser optics, Light wave propagation, Optical imaging

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Proceedings Article | 4 May 2004 Paper

AUC-based resolution in optical coherence tomography

Jason O'Daniel, Tony Delemos, Pascale Parrein, A. Akcay, Kit-Iu Cheong, Kye Lee, Eric Clarkson, Jannick Rolland

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.535935

KEYWORDS: Optical coherence tomography, Optical resolution, Medical imaging, Image resolution, Point spread functions, Interferometry, Coherence (optics), Tissue optics, Mirrors, Tissues

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Proceedings Article | 4 May 2004 Paper

Using Fisher information to compute ideal-observer performance on detection tasks

Fangfang Shen, Eric Clarkson

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.534298

KEYWORDS: Signal detection, Statistical analysis, Imaging systems, Medical imaging, Receivers, Electronic filtering, Applied mathematics, Biomedical optics, Image quality, Computing systems

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In medical imaging, signal detection is one of the most important tasks. A common way to evaluate the performance of an imaging system for a signal-detection task is to calculate the detectability of the ideal observer. Since the detectability of an ideal observer is not always easy to calculate, it is useful to have approximations for it. These approximations can also be used to check the bias of numerical computations of the ideal-observer detectability. For signal detection tasks, we usually have two probability densities for the data vector, the signal-absent density and the signal-present density. In this work, we use a single probability density with a variable scalar or vector parameter to represent the corresponding densities under the two hypotheses. The ideal-observer detectability is derived from the area under the receiver operating characteristic curve of the ideal observer for the given detection task. We have found that we can develop expansions for the square of this detectability as a function of the signal parameter, and that the lowest order expansions involve the Fisher information matrix for the problem of estimating the signal parameter. There are four basic methods we have considered for deriving such expansions. We compute these approximations to ideal-observer detectability for several cases. We compare these to the exact detectability values for these same cases, derived from results in previous work, to examine the usefulness of these approaches. The idea of using one parameterized probability density function is introduced in order to relate detection performance to estimation performance. Even without an analytical expression for ideal-observer detectability we are able to compute analytical forms for its derivatives in terms of the Fisher information matrix and similarly defined statistical moments. The results suggest that there is a connection between the performance of a system on signal-detection tasks and signal-estimation tasks.

Proceedings Article | 4 May 2004 Paper

Efficient channels for the ideal observer

Subok Park, Eric Clarkson, Harrison Barrett, Matthew Kupinski

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.531614

KEYWORDS: Imaging systems, Signal detection, Californium, Monte Carlo methods, Image processing, Numerical analysis, Medical imaging, Performance modeling, Statistical analysis, Fourier transforms

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Proceedings Article | 22 May 2003 Paper

Optimizing imaging hardware for estimation tasks

John Hoppin, Eric Clarkson, Kevin Gross, Matthew Kupinski

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480337

KEYWORDS: Imaging systems, Signal detection, Medical imaging, Monte Carlo methods, Tumors, Image resolution, Systems modeling, Image quality, Interference (communication), Biomedical optics

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Proceedings Article | 22 May 2003 Paper

Assessing the accuracy of estimates of the likelihood ratio

John Hoppin, Eric Clarkson, Matthew Kupinski

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480339

KEYWORDS: Statistical analysis, Signal detection, Imaging systems, Error analysis, Monte Carlo methods, Sensors, Electronic filtering, Signal to noise ratio, Receivers, Detector arrays

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Proceedings Article | 22 May 2003 Paper

Evaluating estimation techniques in medical imaging without a gold standard: experimental validation

Harrison Barrett, Lars Furenlid, Benjamin Gershman, Todd Peterson, John Hoppin, Donald Wilson, Eric Clarkson, Matthew Kupinski, George Kastis

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480330

KEYWORDS: Gold, Single photon emission computed tomography, Statistical analysis, Image quality standards, Imaging systems, Medical imaging, Standards development, Heart, Computed tomography, Analytical research

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Proceedings Article | 22 May 2003 Paper

Optimizing a multiple-pinhole SPECT system using the ideal observer

Todd Peterson, Eric Clarkson, Matthew Kupinski, Kevin Gross

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480336

KEYWORDS: Imaging systems, 3D modeling, Monte Carlo methods, Systems modeling, Sensors, Signal detection, Single photon emission computed tomography, 3D image processing, Image quality, Medical imaging

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Proceedings Article | 3 July 2001 Paper

Feature-extraction method based on the ideal observer

Harrison Barrett, Hongbin Zhang, Eric Clarkson

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431116

KEYWORDS: Lithium, Stereolithography, Electronic filtering, Medical imaging, Image processing, Binary data

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Proceedings Article | 3 July 2001 Paper

Nonlinear discriminant analysis

Harrison Barrett, Eric Clarkson, Hongbin Zhang

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431117

KEYWORDS: Stereolithography, Lithium, Statistical analysis, Wavelet transforms, Medical imaging, Image processing, Discrete wavelet transforms, Feature extraction, Wavelets

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Proceedings Article | 28 June 2001 Paper

Megalopinakophobia: its symptoms and cures

Kyle Myers, Harrison Barrett, Hongbin Zhang, Brandon Gallas, Eric Clarkson

Proceedings Volume 4320, (2001) https://doi.org/10.1117/12.430869

KEYWORDS: Signal to noise ratio, Sensors, X-rays, Signal detection, Radiography, X-ray detectors, X-ray imaging, Statistical analysis, Interference (communication), Monte Carlo methods

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Proceedings Article | 28 June 2001 Paper

Analytical approximations to the Hotelling trace for digital x-ray detectors

Angel Pineda, Eric Clarkson, Harrison Barrett

Proceedings Volume 4320, (2001) https://doi.org/10.1117/12.430932

KEYWORDS: Sensors, X-ray detectors, Signal to noise ratio, Fourier transforms, Signal processing, Digital signal processing, Signal detection, Imaging systems, Medical imaging, Convolution

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Proceedings Article | 19 July 1999 Paper

Imaging methods that ensure positive images

Eric Clarkson, John Denny

Proceedings Volume 3749, (1999) https://doi.org/10.1117/12.355059

KEYWORDS: Image restoration, Radiology, Reconstruction algorithms, Expectation maximization algorithms, Imaging systems, Mathematics, Medical imaging, Data compression, Digital imaging, Optical spheres

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Proceedings Article | 28 May 1999 Paper

Synthetic collimator for 2D and 3D imaging

Harrison Barrett, Eric Clarkson, Donald Wilson

Proceedings Volume 3659, (1999) https://doi.org/10.1117/12.349478

KEYWORDS: Collimators, Sensors, Detector arrays, 3D image processing, Imaging systems, Image resolution, Expectation maximization algorithms, 3D image reconstruction, Photons, 3D modeling

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Proceedings Article | 24 July 1998 Paper

Synthesizing a parallel-projection image from pinhole data

Harrison Barrett, Eric Clarkson, Donald Wilson

Proceedings Volume 3336, (1998) https://doi.org/10.1117/12.317024

KEYWORDS: Sensors, Collimators, Detector arrays, Gamma radiation, Photons, Data analysis, Image resolution, Error analysis, Silicon, Cancer

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Proceedings Article | 21 April 1998 Paper

Synthesis of biomedical tissue

Alexei Goon, Eric Clarkson, Liyun Yu, Jannick Rolland

Proceedings Volume 3340, (1998) https://doi.org/10.1117/12.306179

KEYWORDS: Image processing, Tissues, Statistical modeling, Breast, Image filtering, Medical imaging, Statistical analysis, Biomedical optics, Distance measurement, Image quality

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Proceedings Article | 21 April 1998 Paper

Some unlikely properties of the likelihood ratio and its logarithm

Craig Abbey, Harrison Barrett, Eric Clarkson

Proceedings Volume 3340, (1998) https://doi.org/10.1117/12.306184

KEYWORDS: Signal detection, Signal to noise ratio, Electronic filtering, Monte Carlo methods, Image quality, Receivers, Imaging systems, Polonium, Binary data, Image sensors

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