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1.
1.L. de Sisternes, A. M. Zysk, J. G. Brankov, and M. N. Wernick, “Development of a computational three-dimensional breast lesion phantom model,” Proc. SPIE 7622, 762205762208 (2010).
http://dx.doi.org/10.1117/12.844501
2.
2.R. Saunders, E. Samei, J. Baker, and D. Delong, “Simulation of mammographic lesions,” Acad. Radiol. 13, 860870 (2006).
http://dx.doi.org/10.1016/j.acra.2006.03.015
3.
3.M. Berks, D. B. da Silva, C. Boggis, and S. Astley, “Evaluating the realism of synthetically generated mammographic lesions: An observer study,” Proc. SPIE 7627, 762704762711 (2010).
http://dx.doi.org/10.1117/12.845543
4.
4.K. Bliznakova, Z. Bliznakov, V. Bravou, Z. Kolitsi, and N. Pallikarakis, “A three-dimensional breast software phantom for mammography simulation,” Phys. Med. Biol. 48, 36993719 (2003).
http://dx.doi.org/10.1088/0031-9155/48/22/006
5.
5.A. Rashidnasab, P. Elangovan, M. Yip, O. Diaz, D. R. Dance, K. C. Young, and K. Wells, “Simulation and assessment of realistic breast lesions using fractal growth models,” Phys. Med. Biol. 58, 56135627 (2013).
http://dx.doi.org/10.1088/0031-9155/58/16/5613
6.
6.A. Rashidnasab, P. Elangovan, D. R. Dance, K. C. Young, M. Yip, O. Diaz, and K. Wells, “Realistic simulation of breast mass appearance using random walk,” Proc. SPIE 8313, 83130L183130L7 (2012).
http://dx.doi.org/10.1117/12.911641
7.
7.L. de Sisternes, “Computer modeling of breast lesions and studies of analyzer-based X-ray imaging,” Ph.D. dissertation ( Illinois Institute of Technology, 2011).
8.
8.X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 10411050 (2006).
http://dx.doi.org/10.1118/1.2174127
9.
9.J. Shorey, “Stochastic simulations for the detection of objects in three dimensional volumes: Applications in medical imaging and ocean acoustics,” Ph.D. dissertation ( Duke University, 2007).
10.
10.A. K. W. Ma, S. Gunn, and D. G. Darambara, “Introducing DeBRa: A detailed breast model for radiological studies,” Phy. Med. Biol. 54, 45334545 (2009).
http://dx.doi.org/10.1088/0031-9155/54/14/010
11.
11.M. N. Wernick, O. Wirjadi, D. Chapman, Z. Zhong, N. P. Galatsanos, Y. Yang, J. G. Brankov, O. Oltulu, M. A. Anastasio, and C. Muehleman, “Multiple-image radiography,” Phys. Med. Biol. 48, 38753895 (2003).
http://dx.doi.org/10.1088/0031-9155/48/23/006
12.
12.J. G. Brankov, M. N. Wernick, Y. Yang, J. Li, C. Muehleman, Z. Zhong, and M. A. Anastasio, “A computed tomography implementation of multiple-image radiography,” Med. Phys. 33, 278289 (2006).
http://dx.doi.org/10.1118/1.2150788
13.
13.K. Muinonen, “Introducing the gaussian shape hypothesis for asteroids and comets,” Astron. Astrophys. 332, 10871098 (1998).
14.
14.K. Muinonen, E. Zubko, J. Tyynelä, Y. G. Shkuratov, and G. Videen, “Light scattering by Gaussian random particles with discrete-dipole approximation,” J. Quant. Spectrosc. Radiat. Transfer 106, 360377 (2007).
http://dx.doi.org/10.1016/j.jqsrt.2007.01.049
15.
15.C. D. Murray, “The physiological principle of minimum work: I. The vascular system and the cost of blood volume,” Proc. Natl. Acad. Sci. 12, 207214 (1926).
http://dx.doi.org/10.1073/pnas.12.3.207
16.
16.M. Zamir and H. Chee, “Branching characteristics of human coronary arteries,” Can. J. Physiol. Pharmacol. 64, 661668 (1986).
http://dx.doi.org/10.1139/y86-109
17.
17.P. C. Johns and M. Yaffe, “X-ray characterization of normal and neoplastic breast tissues,” Phys. Med. Biol. 32, 675695 (1987).
http://dx.doi.org/10.1088/0031-9155/32/6/002
18.
18.J. M. Boone and J. A. Seibert, “An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 240 kV,” Med.Phys. 24, 16611671 (1997).
http://dx.doi.org/10.1118/1.597953
19.
19.J. M. Boone, T. R. Fewell, and R. J. Jennings, “Molybdenum, rhodium, and tungsten anode spectral models using interpolated polynomials with application to mammography,” Med. Phys. 24, 18631864 (1997).
http://dx.doi.org/10.1118/1.598100
20.
20.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 (Medical Physics Publishing, Madison, WI, 2001), pp. 212218.
21.
21.M. Heath, K. Bowyer, D. Kopans, W. P. Kegelmeyer, R. Moore, K. Chang, and S. M. Kumaran, “Current status of the digital database for screening mammography,” in Proceedings of the Fourth International Workshop on Digital Mammography (Kluwer Academic Publishers, Dordrecht, Netherlands, 1998), pp. 456460.
22.
22.F. L. Bookstein, “Principal warps: Thin plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 567585 (1989).
http://dx.doi.org/10.1109/34.24792
23.
23.J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, “Convergence properties of the Nelder-Mead simplex method in low dimensions,” J. Optim. 9, 112147 (1998).
http://dx.doi.org/10.1137/S1052623496303470
24.
24.H. T. Apsimon, H. J. Stewart, and W. J. Williams, “Recording the gross outlines of breast tumours a pathological assessment of the accuracy of radiographs of breast cancer,” Br. J. Cancer 22, 4046 (1968).
http://dx.doi.org/10.1038/bjc.1968.6
25.
25.M. Lanyi, Mammography: Diagnosis and Pathological Analysis (Springer, New York, NY, 2003), ISBN 3-540-43134-9.
26.
26.L. Tabar, T. Tot, and P. B. Dean, “Breast cancer,” in The Art and Science of Early Detection with Mammography (Thieme, Stuttgart, 2005), ISBN 3-13-135371-6.
27.
27.G. Mazy, L. J. van Bogaert, and L. Jeahmart, “La définition de l’image spiculaire des cancers mammaires,” J. Radiol. Electrol. 56, 312313 (1975).
28.
28.L. J. van Bogaert, J. Hermans, and S. G. Obstet, “Importance of spicules on clinical staging of carcinoma of the breast,” Surg., Gynecol. Obstet. 144, 356358 (1977).
29.
29.Z. Huo, M. L. Giger, C. J. Vyborny, U. Bick, P. Lu, D. E. Wolverton, and R. A. Schmidt, “Analysis of spiculation in the computerized classification of mammographic masses,” Med. Phys. 22, 15691579 (1995).
http://dx.doi.org/10.1118/1.597626
30.
30.Z. Huo, M. L. Giger, C. J. vyborny, D. E. Wolverton, R. A. Schmidt, and K. Doi, “Automated computerized classification of malignant and benign masses on digitized mammograms,” Acad. Radiol. 5, 155168 (1998).
http://dx.doi.org/10.1016/S1076-6332(98)80278-X
31.
31.Z. Huo, M. L. Giger, C. J. Vyborny, D. E. Wolverton, and C. E. Metz, “Computerized classification of benign and malignant masses on digitized mammograms: A study of robustness,” Acad. Radiol. 7, 10771084 (2000).
http://dx.doi.org/10.1016/s1076-6332(00)80060-4
32.
32.D. D. Dorfman, K. S. Berbaum, and C. E. Metz, “Receiver operating characteristic rating analysis: Generalization to the population of readers and patients with the jackknife method,” Invest. Radiol. 27, 723731 (1992).
http://dx.doi.org/10.1097/00004424-199209000-00015
33.
33.D. D. Dorfman, K. S. Berbaum, R. V. Lenth, Y. F. Chen, and B. A. Donaghy, “Monte Carlo validation of a multireader method for receiver operating characteristic discrete rating data: Factorial experimental design,” Acad. Radiol. 5, 591602 (1998).
http://dx.doi.org/10.1016/S1076-6332(98)80294-8
34.
34.S. L. Hillis and K. S. Berbaum, “Power estimation for the Dorfman-Berbaum-Metz method,” Acad. Radiol. 11, 12601273 (2004).
http://dx.doi.org/10.1016/j.acra.2004.08.009
35.
35.S. L. Hillis, N. A. Obuchowski, K. M. Schartz, and K. S. Berbaum, “A comparison of the Dorfman-Berbaum-Metz and Obuchowski-Rockette methods for receiver operating characteristic (ROC) data,” Stat. Med. 24, 15791607 (2005).
http://dx.doi.org/10.1002/sim.2024
36.
36.S. L. Hillis, “Monte Carlo validation of the Dorfman-Berbaum-Metz method using normalized pseudovalues and less data-based model simplification,” Acad. Radiol. 12, 15341541 (2005).
http://dx.doi.org/10.1016/j.acra.2005.07.012
37.
37.S. L. Hillis, “A comparison of denominator degrees of freedom for multiple observer ROC analysis,” Stat. Med. 26, 596619 (2007).
http://dx.doi.org/10.1002/sim.2532
38.
38.S. L. Hillis, K. S. Berbaum, and C. E. Metz, “Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis,” Acad. Radiol. 15, 647661 (2008).
http://dx.doi.org/10.1016/j.acra.2007.12.015
39.
39. Throughout the paper we will describe clinical mammograms exhibiting actual tumors as “real” mammograms, and normal clinical images modified to include simulated masses as “hybrid” images.
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/content/aapm/journal/medphys/42/2/10.1118/1.4905232
2015-02-02
2016-09-26

Abstract

To develop algorithms for creating realistic three-dimensional (3D) simulated breast masses and embedding them within actual clinical mammograms. The proposed techniques yield high-resolution simulated breast masses having randomized shapes, with user-defined mass type, size, location, and shape characteristics.

The authors describe a method of producing 3D digital simulations of breast masses and a technique for embedding these simulated masses within actual digitized mammograms. Simulated 3D breast masses were generated by using a modified stochastic Gaussian random sphere model to generate a central tumor mass, and an iterative fractal branching algorithm to add complex spicule structures. The simulated masses were embedded within actual digitized mammograms. The authors evaluated the realism of the resulting hybrid phantoms by generating corresponding left- and right-breast image pairs, consisting of one breast image containing a real mass, and the opposite breast image of the same patient containing a similar simulated mass. The authors then used computer-aided diagnosis (CAD) methods and expert radiologist readers to determine whether significant differences can be observed between the real and hybrid images.

The authors found no statistically significant difference between the CAD features obtained from the real and simulated images of masses with either spiculated or nonspiculated margins. Likewise, the authors found that expert human readers performed very poorly in discriminating their hybrid images from real mammograms.

The authors’ proposed method permits the realistic simulation of 3D breast masses having user-defined characteristics, enabling the creation of a large set of hybrid breast images containing a well-characterized mass, embedded within real breast background. The computational nature of the model makes it suitable for detectability studies, evaluation of computer aided diagnosis algorithms, and teaching purposes.

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