Abstract

We studied the influence of signal variability on human and model observers for detection tasks with realistic simulated masses superimposed on real patient mammographic backgrounds and synthesized mammographic backgrounds (clustered lumpy backgrounds, CLB). Results under the signal-known-exactly (SKE) paradigm were compared with signal-known-statistically (SKS) tasks for which the observers did not have prior knowledge of the shape or size of the signal. Human observers' performance did not vary significantly when benign masses were superimposed on real images or on CLB. Uncertainty and variability in signal shape did not degrade human performance significantly compared with the SKE task, while variability in signal size did. Implementation of appropriate internal noise components allowed the fit of model observers to human performance.

© 2009 Optical Society of America

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2008 (2)

C. Castella, K. Kinkel, F. Descombes, M. P. Eckstein, P.-E. Sottas, F. R. Verdun, and F. O. Bochud, “Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm,” Opt. Express 16, 7595-7607 (2008).
[CrossRef] [PubMed]

C. Castella, K. Kinkel, M. P. Eckstein, C. K. Abbey, F. R. Verdun, R. S. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: signal variations and performance changes for human and model observers,” Proc. SPIE 6917, 69170K (2008).
[CrossRef]

2007 (6)

C. Castella, C. K. Abbey, M. P. Eckstein, F. R. Verdun, K. Kinkel, and F. O. Bochud, “Human linear template with mammographic backgrounds estimated with a genetic algorithm,” J. Opt. Soc. Am. A 24, B1-B12 (2007).
[CrossRef]

A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: effect of spectrum exponents,” J. Opt. Soc. Am. A 24, B52-B60 (2007).
[CrossRef]

D. S. Brettle, E. Berry, and M. A. Smith, “The effect of experience on detectability in local area anatomical noise,” Br. J. Radiol. 80, 186-193 (2007).
[CrossRef]

B. D. Gallas, G. A. Pennello, and K. J. Myers, “Multireader multicase variance analysis for binary data,” J. Opt. Soc. Am. A 24, B70-B80 (2007).
[CrossRef]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312-3322 (2007).
[CrossRef] [PubMed]

Y. Jiang, D. L. Miglioretti, C. E. Metz, and R. A. Schmidt, “Breast cancer detection rate: designing imaging trials to demonstrate improvements,” Radiology 243, 360-367 (2007).
[CrossRef] [PubMed]

2006 (2)

2005 (1)

Y. Zhang, B. P. Pham, and M. P. Eckstein, “Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization,” Acad. Radiol. 12, 324-336 (2005).
[CrossRef] [PubMed]

2004 (3)

A. E. Burgess, “On the noise variance of a digital mammography system,” Med. Phys. 31, 1987-1995 (2004).
[CrossRef] [PubMed]

R. Saunders and E. Samei, “Characterization of breast masses for simulation purposes,” Proc. SPIE 5372, 242-250 (2004).
[CrossRef]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 459-474 (2004).
[CrossRef] [PubMed]

2003 (1)

M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123-134 (2003).
[CrossRef]

2002 (8)

M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13-24 (2002).
[CrossRef]

A. J. Ahumada, Jr., “Classification image weights and internal noise level estimation,” J. Vision 2, 121-131 (2002).
[CrossRef]

J. A. Solomon, “Noise reveals visual mechanisms of detection and discrimination,” J. Vision 2, 105-120 (2002).
[CrossRef]

R. F. Murray, P. J. Bennett, and A. B. Sekuler, “Optimal methods for calculating classification images: Weighted sums,” J. Vision 2, 79-104 (2002).
[CrossRef]

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human-observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-36 (2002).
[CrossRef]

C. K. Abbey and M. P. Eckstein, “Classification image analysis: Estimation and statistical inference for two-alternative forced-choice experiments,” J. Vision 2, 66-78 (2002).
[CrossRef]

C. K. Abbey and M. P. Eckstein, “Optimal shifted estimates of human-observer templates in two-alternative forced choice experiments,” IEEE Trans. Med. Imaging 21, 429-440 (2002).
[CrossRef] [PubMed]

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-35 (2002).
[CrossRef]

2001 (4)

C. K. Abbey and M. P. Eckstein, “Maximum-likelihood and maximum-a-posteriori estimates of human-observer templates,” Proc. SPIE 4324, 114-122 (2001).
[CrossRef]

M. P. Eckstein and C. K. Abbey, “Model observers for signal-known-statistically tasks (SKS),” Proc. SPIE 4324, 91-102 (2001).
[CrossRef]

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419-437 (2001).
[CrossRef] [PubMed]

C. K. Abbey and H. H. Barrett, “Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A 18, 473-487 (2001).
[CrossRef]

2000 (2)

S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
[CrossRef] [PubMed]

F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds,” J. Opt. Soc. Am. A 17, 193-205 (2000).
[CrossRef]

1999 (2)

1997 (2)

M. P. Eckstein, A. J. Ahumada, and A. B. Watson, “Image discrimination models predict signal detection in natural medical image backgrounds,” Proc. SPIE 3016, 44-56 (1997).
[CrossRef]

P. F. Judy, M. F. Kijewski, and R. G. Svensson, “Observer detection performance loss: target-size uncertainty,” Proc. SPIE 3036, 39-47 (1997).
[CrossRef]

1995 (1)

A. E. Burgess, “Comparison of receiver operating characteristics and forced choice observer performance measurement methods,” Med. Phys. 22, 643-655 (1995).
[CrossRef] [PubMed]

1994 (1)

1992 (1)

1987 (2)

P. G. J. Barten, “The SQRI method: a new method for the evaluation of visible resolution on a display,” Proc. S.I.D. 28, 253-262 (1987).

K. J. Myers and H. H. Barrett, “Addition of a channel mechanism to the ideal-observer model,” J. Opt. Soc. Am. A 4, 2447-2457 (1987).
[CrossRef] [PubMed]

1985 (1)

1984 (1)

1981 (1)

A. E. Burgess, R. F. Wagner, R. J. Jennings, and H. B. Barlow, “Efficiency of human visual signal discrimination,” Science 214, 93-94 (1981).
[CrossRef] [PubMed]

Abbey, C. K.

C. Castella, K. Kinkel, M. P. Eckstein, C. K. Abbey, F. R. Verdun, R. S. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: signal variations and performance changes for human and model observers,” Proc. SPIE 6917, 69170K (2008).
[CrossRef]

C. Castella, C. K. Abbey, M. P. Eckstein, F. R. Verdun, K. Kinkel, and F. O. Bochud, “Human linear template with mammographic backgrounds estimated with a genetic algorithm,” J. Opt. Soc. Am. A 24, B1-B12 (2007).
[CrossRef]

Y. Zhang, C. K. Abbey, and M. P. Eckstein, “Adaptive detection mechanisms in globally statistically nonstationary-oriented noise,” J. Opt. Soc. Am. A 23, 1549-1558 (2006).
[CrossRef]

M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123-134 (2003).
[CrossRef]

M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13-24 (2002).
[CrossRef]

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-35 (2002).
[CrossRef]

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human-observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-36 (2002).
[CrossRef]

C. K. Abbey and M. P. Eckstein, “Classification image analysis: Estimation and statistical inference for two-alternative forced-choice experiments,” J. Vision 2, 66-78 (2002).
[CrossRef]

C. K. Abbey and M. P. Eckstein, “Optimal shifted estimates of human-observer templates in two-alternative forced choice experiments,” IEEE Trans. Med. Imaging 21, 429-440 (2002).
[CrossRef] [PubMed]

C. K. Abbey and H. H. Barrett, “Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A 18, 473-487 (2001).
[CrossRef]

C. K. Abbey and M. P. Eckstein, “Maximum-likelihood and maximum-a-posteriori estimates of human-observer templates,” Proc. SPIE 4324, 114-122 (2001).
[CrossRef]

M. P. Eckstein and C. K. Abbey, “Model observers for signal-known-statistically tasks (SKS),” Proc. SPIE 4324, 91-102 (2001).
[CrossRef]

F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds,” J. Opt. Soc. Am. A 17, 193-205 (2000).
[CrossRef]

F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Express 4, 33-43 (1999).
[CrossRef] [PubMed]

M. P. Eckstein, C. K. Abbey, and F. O. Bochud, “A practical guide to model observers for visual detection in synthetic and natural noisy images,” in Handbook of Medical Imaging, Vol. 1, Physics and psychophysics, J.Beutel, H.L.Kundel, R.L.Van Metter, eds. (SPIE Press, 2000), pp. 593-628.

Ahumada, A. J.

A. J. Ahumada, Jr., “Classification image weights and internal noise level estimation,” J. Vision 2, 121-131 (2002).
[CrossRef]

M. P. Eckstein, A. J. Ahumada, and A. B. Watson, “Image discrimination models predict signal detection in natural medical image backgrounds,” Proc. SPIE 3016, 44-56 (1997).
[CrossRef]

Albagli, D.

S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
[CrossRef] [PubMed]

Baker, J.

R. Saunders, E. Samei, J. Baker, and D. Delong, “Simulation of mammographic lesions,” Acad. Radiol. 13, 860-870 (2006).
[CrossRef] [PubMed]

Barlow, H. B.

A. E. Burgess, R. F. Wagner, R. J. Jennings, and H. B. Barlow, “Efficiency of human visual signal discrimination,” Science 214, 93-94 (1981).
[CrossRef] [PubMed]

Barrett, H. H.

Barten, P. G. J.

P. G. J. Barten, “The SQRI method: a new method for the evaluation of visible resolution on a display,” Proc. S.I.D. 28, 253-262 (1987).

Baydush, A. H.

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human-observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-36 (2002).
[CrossRef]

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-35 (2002).
[CrossRef]

Bennett, P. J.

R. F. Murray, P. J. Bennett, and A. B. Sekuler, “Optimal methods for calculating classification images: Weighted sums,” J. Vision 2, 79-104 (2002).
[CrossRef]

Berry, E.

D. S. Brettle, E. Berry, and M. A. Smith, “The effect of experience on detectability in local area anatomical noise,” Br. J. Radiol. 80, 186-193 (2007).
[CrossRef]

Bochud, F. O.

Borgstrom, M. C.

Bowyer, K.

M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer, “The Digital Database For Screening Mammography,” in Proceedings of the Fifth International Workshop on Digital Mammography, M.J.Yaffe, ed (Medical Physics Publishing, 2001), pp. 212-218.

Brettle, D. S.

D. S. Brettle, E. Berry, and M. A. Smith, “The effect of experience on detectability in local area anatomical noise,” Br. J. Radiol. 80, 186-193 (2007).
[CrossRef]

Broeders, M.

N. Perry, M. Broeders, C. de Wolf, S. Törnberg, R. Holland, and L. von Karsa, European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis, 4th ed. (Office for Official Publications of the European Communities, 2006).

Burgess, A. E.

A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: effect of spectrum exponents,” J. Opt. Soc. Am. A 24, B52-B60 (2007).
[CrossRef]

A. E. Burgess, “On the noise variance of a digital mammography system,” Med. Phys. 31, 1987-1995 (2004).
[CrossRef] [PubMed]

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419-437 (2001).
[CrossRef] [PubMed]

A. E. Burgess, “Comparison of receiver operating characteristics and forced choice observer performance measurement methods,” Med. Phys. 22, 643-655 (1995).
[CrossRef] [PubMed]

A. E. Burgess, “Statistically defined backgrounds: Performance of a modified nonprewhitening observer,” J. Opt. Soc. Am. A 11, 1237-1242 (1994).
[CrossRef]

A. E. Burgess and H. Ghandeharian, “Visual signal detection. I. Ability to use phase information,” J. Opt. Soc. Am. A 1, 900-905 (1984).
[CrossRef] [PubMed]

A. E. Burgess, R. F. Wagner, R. J. Jennings, and H. B. Barlow, “Efficiency of human visual signal discrimination,” Science 214, 93-94 (1981).
[CrossRef] [PubMed]

Castella, C.

Catarious, D. M.

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-35 (2002).
[CrossRef]

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human-observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-36 (2002).
[CrossRef]

de Wolf, C.

N. Perry, M. Broeders, C. de Wolf, S. Törnberg, R. Holland, and L. von Karsa, European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis, 4th ed. (Office for Official Publications of the European Communities, 2006).

Delong, D.

R. Saunders, E. Samei, J. Baker, and D. Delong, “Simulation of mammographic lesions,” Acad. Radiol. 13, 860-870 (2006).
[CrossRef] [PubMed]

Descombes, F.

D'Orsi, C. J.

S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
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C. Castella, K. Kinkel, F. Descombes, M. P. Eckstein, P.-E. Sottas, F. R. Verdun, and F. O. Bochud, “Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm,” Opt. Express 16, 7595-7607 (2008).
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C. Castella, C. K. Abbey, M. P. Eckstein, F. R. Verdun, K. Kinkel, and F. O. Bochud, “Human linear template with mammographic backgrounds estimated with a genetic algorithm,” J. Opt. Soc. Am. A 24, B1-B12 (2007).
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Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312-3322 (2007).
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Y. Zhang, C. K. Abbey, and M. P. Eckstein, “Adaptive detection mechanisms in globally statistically nonstationary-oriented noise,” J. Opt. Soc. Am. A 23, 1549-1558 (2006).
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Y. Zhang, B. P. Pham, and M. P. Eckstein, “Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization,” Acad. Radiol. 12, 324-336 (2005).
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Y. Zhang, B. T. Pham, and M. P. Eckstein, “Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 459-474 (2004).
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M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123-134 (2003).
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M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13-24 (2002).
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C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-35 (2002).
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C. K. Abbey and M. P. Eckstein, “Classification image analysis: Estimation and statistical inference for two-alternative forced-choice experiments,” J. Vision 2, 66-78 (2002).
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C. K. Abbey and M. P. Eckstein, “Optimal shifted estimates of human-observer templates in two-alternative forced choice experiments,” IEEE Trans. Med. Imaging 21, 429-440 (2002).
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C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human-observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-36 (2002).
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F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds,” J. Opt. Soc. Am. A 17, 193-205 (2000).
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F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Express 4, 33-43 (1999).
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M. P. Eckstein, A. J. Ahumada, and A. B. Watson, “Image discrimination models predict signal detection in natural medical image backgrounds,” Proc. SPIE 3016, 44-56 (1997).
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Eng, J.

J. Eng, “ROC analysis: web-based calculator for ROC curves,” http://www.jrocfit.org.

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C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human-observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-36 (2002).
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C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-35 (2002).
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Ghandeharian, H.

Granfors, P. R.

S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
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S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
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S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
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A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: effect of spectrum exponents,” J. Opt. Soc. Am. A 24, B52-B60 (2007).
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A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419-437 (2001).
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S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
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M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer, “The Digital Database For Screening Mammography,” in Proceedings of the Fifth International Workshop on Digital Mammography, M.J.Yaffe, ed (Medical Physics Publishing, 2001), pp. 212-218.

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S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
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Y. Jiang, D. L. Miglioretti, C. E. Metz, and R. A. Schmidt, “Breast cancer detection rate: designing imaging trials to demonstrate improvements,” Radiology 243, 360-367 (2007).
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M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer, “The Digital Database For Screening Mammography,” in Proceedings of the Fifth International Workshop on Digital Mammography, M.J.Yaffe, ed (Medical Physics Publishing, 2001), pp. 212-218.

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S. Muller, “Full-field digital mammography designed as a complete system,” Eur. J. Radiol. 39, 25-34 (1999).
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R. F. Murray, P. J. Bennett, and A. B. Sekuler, “Optimal methods for calculating classification images: Weighted sums,” J. Vision 2, 79-104 (2002).
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Pennello, G. A.

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N. Perry, M. Broeders, C. de Wolf, S. Törnberg, R. Holland, and L. von Karsa, European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis, 4th ed. (Office for Official Publications of the European Communities, 2006).

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M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123-134 (2003).
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M. P. Eckstein, B. Pham, and C. K. Abbey, “Effect of image compression for model and human observers in signal-known-statistically tasks,” Proc. SPIE 4686, 13-24 (2002).
[CrossRef]

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Y. Zhang, B. P. Pham, and M. P. Eckstein, “Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization,” Acad. Radiol. 12, 324-336 (2005).
[CrossRef] [PubMed]

Pham, B. T.

Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312-3322 (2007).
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Y. Zhang, B. T. Pham, and M. P. Eckstein, “Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 459-474 (2004).
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Rolland, J. P.

Samei, E.

C. Castella, K. Kinkel, M. P. Eckstein, C. K. Abbey, F. R. Verdun, R. S. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: signal variations and performance changes for human and model observers,” Proc. SPIE 6917, 69170K (2008).
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R. Saunders, E. Samei, J. Baker, and D. Delong, “Simulation of mammographic lesions,” Acad. Radiol. 13, 860-870 (2006).
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R. Saunders and E. Samei, “Characterization of breast masses for simulation purposes,” Proc. SPIE 5372, 242-250 (2004).
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Saunders, R. S.

C. Castella, K. Kinkel, M. P. Eckstein, C. K. Abbey, F. R. Verdun, R. S. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: signal variations and performance changes for human and model observers,” Proc. SPIE 6917, 69170K (2008).
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Y. Jiang, D. L. Miglioretti, C. E. Metz, and R. A. Schmidt, “Breast cancer detection rate: designing imaging trials to demonstrate improvements,” Radiology 243, 360-367 (2007).
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Sekuler, A. B.

R. F. Murray, P. J. Bennett, and A. B. Sekuler, “Optimal methods for calculating classification images: Weighted sums,” J. Vision 2, 79-104 (2002).
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C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human-observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-36 (2002).
[CrossRef]

C. K. Abbey, M. P. Eckstein, S. S. Shimozaki, A. H. Baydush, D. M. Catarious, and C. E. Floyd, “Human observer templates for detection of a simulated lesion in mammographic images,” Proc. SPIE 4686, 25-35 (2002).
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D. S. Brettle, E. Berry, and M. A. Smith, “The effect of experience on detectability in local area anatomical noise,” Br. J. Radiol. 80, 186-193 (2007).
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J. A. Solomon, “Noise reveals visual mechanisms of detection and discrimination,” J. Vision 2, 105-120 (2002).
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Suryanarayanan, S.

S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
[CrossRef] [PubMed]

Svensson, R. G.

P. F. Judy, M. F. Kijewski, and R. G. Svensson, “Observer detection performance loss: target-size uncertainty,” Proc. SPIE 3036, 39-47 (1997).
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S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
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N. Perry, M. Broeders, C. de Wolf, S. Törnberg, R. Holland, and L. von Karsa, European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis, 4th ed. (Office for Official Publications of the European Communities, 2006).

Vedantham, S.

S. Vedantham, A. Karellas, S. Suryanarayanan, D. Albagli, S. Han, E. J. Tkaczyk, C. E. Landberg, B. Opsahl-Ong, P. R. Granfors, I. Levis, C. J. D'Orsi, and R. E. Hendrick, “Full breast digital mammography with an amorphous silicon-based flat panel detector: Physical characteristics of a clinical prototype,” Med. Phys. 27, 558-567 (2000).
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Verdun, F. R.

von Karsa, L.

N. Perry, M. Broeders, C. de Wolf, S. Törnberg, R. Holland, and L. von Karsa, European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis, 4th ed. (Office for Official Publications of the European Communities, 2006).

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M. P. Eckstein, A. J. Ahumada, and A. B. Watson, “Image discrimination models predict signal detection in natural medical image backgrounds,” Proc. SPIE 3016, 44-56 (1997).
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Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312-3322 (2007).
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Y. Zhang, C. K. Abbey, and M. P. Eckstein, “Adaptive detection mechanisms in globally statistically nonstationary-oriented noise,” J. Opt. Soc. Am. A 23, 1549-1558 (2006).
[CrossRef]

Y. Zhang, B. P. Pham, and M. P. Eckstein, “Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization,” Acad. Radiol. 12, 324-336 (2005).
[CrossRef] [PubMed]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 459-474 (2004).
[CrossRef] [PubMed]

M. P. Eckstein, Y. Zhang, B. Pham, and C. K. Abbey, “Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks,” Proc. SPIE 5034, 123-134 (2003).
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Acad. Radiol. (2)

R. Saunders, E. Samei, J. Baker, and D. Delong, “Simulation of mammographic lesions,” Acad. Radiol. 13, 860-870 (2006).
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Y. Zhang, B. P. Pham, and M. P. Eckstein, “Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization,” Acad. Radiol. 12, 324-336 (2005).
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Br. J. Radiol. (1)

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S. Muller, “Full-field digital mammography designed as a complete system,” Eur. J. Radiol. 39, 25-34 (1999).
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C. K. Abbey and M. P. Eckstein, “Optimal shifted estimates of human-observer templates in two-alternative forced choice experiments,” IEEE Trans. Med. Imaging 21, 429-440 (2002).
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Y. Zhang, B. T. Pham, and M. P. Eckstein, “Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms,” IEEE Trans. Med. Imaging 23, 459-474 (2004).
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J. Vision (4)

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C. K. Abbey and M. P. Eckstein, “Classification image analysis: Estimation and statistical inference for two-alternative forced-choice experiments,” J. Vision 2, 66-78 (2002).
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Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312-3322 (2007).
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Opt. Express (2)

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Proc. SPIE (10)

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Figures (8)

Fig. 1
Fig. 1

Backgrounds, mass type, and signal conditions for the 13 2-AFC detection task experiments. Benign and Malignant characterize simulated masses. SKE stands for an experiment with a single signal of given shape and size and SKS an experiment with a signal with variable shape but a given size, except for the last experiment where both shape and size were variable.

Fig. 2
Fig. 2

(a) Example of CLB with a digitally embedded benign simulated mass. (b) Real mammogram ROI with a digitally embedded malignant simulated mass. Signal contrast has been strongly increased for illustration purposes.

Fig. 3
Fig. 3

(a) Human observers’ performance ( d ) for the SKE tasks with the 6.5 mm benign simulated masses. The rightmost values for each figure (generic observer, Gen. Obs.) were obtained by pooling all observer data. The error bars represent the 95% confidence interval. (b) Same for the SKS tasks.

Fig. 4
Fig. 4

Generic human observer results for the size uncertainty task with benign simulated masses (open circles). For comparison, the performance in the SKS experiments with fixed size signals are shown (black squares). Error bars represent the 95% confidence interval.

Fig. 5
Fig. 5

(a) Human observers’ performance ( d ) for the SKE tasks with the 6.5 mm malignant simulated masses. The rightmost values for each figure (generic observer, Gen. Obs.) were obtained by pooling all observers data. The error bars represent the 95% confidence interval. (b) Same for the SKS tasks.

Fig. 6
Fig. 6

RMSE in d units between the generic human observer and the different model observers for SKE tasks for noiseless (shaded) and noise-level-optimized (black) models with (a) benign and (b) malignant simulated masses. Stars indicate performance levels that are significantly different from humans (F-test, p < 0.05 ) [44].

Fig. 7
Fig. 7

Templates derived for the NPW, NPWE, CH with Gabor channels, and HLT models for the SKE tasks with CLB and benign (upper row) and malignant (lower row) simulated masses. HLT estimated for the tasks with real backgrounds are shown in the last column.

Fig. 8
Fig. 8

Examples of individual HLT (SKE task, 6.5 mm benign simulated masses, CLB). The leftmost template is the one corresponding to the generic observer.

Tables (2)

Tables Icon

Table 1 Generic Observer Performance ( d ) and 95% Confidence Interval for the 13 Different Tasks Involving Real or CLB Backgrounds, SKE or SKS Detection, and Simulated Mass Size of 6.5, 9.5 or 5.5 9.5 mm

Tables Icon

Table 2 RMSE overall in d Units Between the Generic Human Observer and the Different Model Observers for the Different Noise Addition Schemes Described in Subsection 2G a

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

λ i = w T g i + ϵ .
w NPW = s ,
w NPWE = E T E s ,
w Hot = K b 1 [ g s g n ] ,
w CH = ( K b , c + K ϵ ) 1 s c .
G ( x , y , Λ , θ , φ ) = exp [ ( x 2 + y 2 ) 2 σ 2 ] cos ( 2 π x Λ + φ ) .
{ ( K b , c , n ) i , j = ( K b , c ) i , j if i j ( K b , c , n ) i , j = ( 1 + p n ) ( K b , c ) i , j if i = j } .
λ i = w CH T g i + k w k ϵ k ,
{ λ i = w T g i + ϵ ϵ N ( 0 , p n σ ext 2 ) } ,
RMSE = 1 3 [ ( d h d m ) CLB , 6.5 mm 2 + ( d h d m ) CLB , 9.5 mm 2 + ( d h d m ) real , 6.5 mm 2 ] ,
{ l + = j = 1 J ( 1 2 π σ j 2 ) exp [ ( λ + , j μ + , j ) 2 2 σ j 2 ] exp [ ( λ , j μ , j ) 2 2 σ j 2 ] l = j = 1 J ( 1 2 π σ j 2 ) exp [ ( λ , j μ + , j ) 2 2 σ j 2 ] exp [ ( λ + , j μ , j ) 2 2 σ j 2 ] ,
F = ( i tasks ( d i , model d i , human ) 2 ) d f numerator ( i tasks var i , human ) d f denominator .

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