Abstract
In localization tasks, an observer is asked to give the location of some target or feature of interest in an image. Scanning linear observer models incorporate the search implicit in this task through convolution of an observer template with the image being evaluated. Such models are becoming increasingly popular as predictors of human performance for validating medical imaging methodology. In addition to convolution, scanning models may utilize internal noise components to model inconsistencies in human observer responses. In this work, we build a probabilistic mathematical model of this process and show how it can, in principle, be used to obtain estimates of the observer template using maximum likelihood methods. The main difficulty of this approach is that a closed form probability distribution for a maximal location response is not generally available in the presence of internal noise. However, for a given image we can generate an empirical distribution of maximal locations using Monte-Carlo sampling. We show that this probability is well approximated by applying an exponential function to the scanning template output. We also evaluate log-likelihood functions on the basis of this approximate distribution. Using 1,000 trials of simulated data as a validation test set, we find that a plot of the approximate log-likelihood function along a single parameter related to the template profile achieves its maximum value near the true value used in the simulation. This finding holds regardless of whether the trials are correctly localized or not. In a second validation study evaluating a parameter related to the relative magnitude of internal noise, only the incorrect localization images produces a maximum in the approximate log-likelihood function that is near the true value of the parameter.
| Original language | English (US) |
|---|---|
| Title of host publication | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
| Publisher | SPIE |
| Volume | 9416 |
| ISBN (Print) | 9781628415063 |
| DOIs | |
| State | Published - 2015 |
| Event | Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment - Orlando, United States Duration: Feb 25 2015 → Feb 26 2015 |
Other
| Other | Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment |
|---|---|
| Country | United States |
| City | Orlando |
| Period | 2/25/15 → 2/26/15 |
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Keywords
- and observer modeling
- localization task
- ramp-spectrum noise
- Scanning linear template
- search models
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Electronic, Optical and Magnetic Materials
- Biomaterials
- Radiology Nuclear Medicine and imaging
Cite this
Approximate maximum likelihood estimation of scanning observer templates. / Abbey, Craig K.; Samuelson, Frank W.; Wunderlich, Adam; Popescu, Lucretiu M.; Eckstein, Miguel P.; Boone, John M.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 9416 SPIE, 2015. 94160O.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Approximate maximum likelihood estimation of scanning observer templates
AU - Abbey, Craig K.
AU - Samuelson, Frank W.
AU - Wunderlich, Adam
AU - Popescu, Lucretiu M.
AU - Eckstein, Miguel P.
AU - Boone, John M
PY - 2015
Y1 - 2015
N2 - In localization tasks, an observer is asked to give the location of some target or feature of interest in an image. Scanning linear observer models incorporate the search implicit in this task through convolution of an observer template with the image being evaluated. Such models are becoming increasingly popular as predictors of human performance for validating medical imaging methodology. In addition to convolution, scanning models may utilize internal noise components to model inconsistencies in human observer responses. In this work, we build a probabilistic mathematical model of this process and show how it can, in principle, be used to obtain estimates of the observer template using maximum likelihood methods. The main difficulty of this approach is that a closed form probability distribution for a maximal location response is not generally available in the presence of internal noise. However, for a given image we can generate an empirical distribution of maximal locations using Monte-Carlo sampling. We show that this probability is well approximated by applying an exponential function to the scanning template output. We also evaluate log-likelihood functions on the basis of this approximate distribution. Using 1,000 trials of simulated data as a validation test set, we find that a plot of the approximate log-likelihood function along a single parameter related to the template profile achieves its maximum value near the true value used in the simulation. This finding holds regardless of whether the trials are correctly localized or not. In a second validation study evaluating a parameter related to the relative magnitude of internal noise, only the incorrect localization images produces a maximum in the approximate log-likelihood function that is near the true value of the parameter.
AB - In localization tasks, an observer is asked to give the location of some target or feature of interest in an image. Scanning linear observer models incorporate the search implicit in this task through convolution of an observer template with the image being evaluated. Such models are becoming increasingly popular as predictors of human performance for validating medical imaging methodology. In addition to convolution, scanning models may utilize internal noise components to model inconsistencies in human observer responses. In this work, we build a probabilistic mathematical model of this process and show how it can, in principle, be used to obtain estimates of the observer template using maximum likelihood methods. The main difficulty of this approach is that a closed form probability distribution for a maximal location response is not generally available in the presence of internal noise. However, for a given image we can generate an empirical distribution of maximal locations using Monte-Carlo sampling. We show that this probability is well approximated by applying an exponential function to the scanning template output. We also evaluate log-likelihood functions on the basis of this approximate distribution. Using 1,000 trials of simulated data as a validation test set, we find that a plot of the approximate log-likelihood function along a single parameter related to the template profile achieves its maximum value near the true value used in the simulation. This finding holds regardless of whether the trials are correctly localized or not. In a second validation study evaluating a parameter related to the relative magnitude of internal noise, only the incorrect localization images produces a maximum in the approximate log-likelihood function that is near the true value of the parameter.
KW - and observer modeling
KW - localization task
KW - ramp-spectrum noise
KW - Scanning linear template
KW - search models
UR - http://www.scopus.com/inward/record.url?scp=84932172216&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84932172216&partnerID=8YFLogxK
U2 - 10.1117/12.2082874
DO - 10.1117/12.2082874
M3 - Conference contribution
AN - SCOPUS:84932172216
SN - 9781628415063
VL - 9416
BT - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
PB - SPIE
ER -