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Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification
1.SEER Cancer Statistics Review, 1975–2007
, edited by S. F. Altekruse
, C. L. Kosary
, M. Krapcho
, N. Neyman
, R. Aminou
, W. Waldron
, J. Ruhl
, N. Howlader
, Z. Tatalovich
, H. Cho
, A. Mariotto
, M. P. Eisner
, D. R. Lewis
, K. Cronin
, H. S. Chen
, E. J. Feuer
, D. G. Stinchcomb
and B. K. Edwards
(National Cancer Institute
, Bethesda, MD
3.W. C. Black, “Computed tomography screening for lung cancer: Review of screening principles and update on current status,” Cancer 110(11), 2370–2384 (2007).
4.C. I. Henschke, D. F. Yankelevitz, D. M. Libby, M. W. Pasmantier, J. P. Smith, and O. S. Miettinen, “Survival of patients with stage I lung cancer detected on CT screening,” N. Engl. J. Med. 355(17), 1763–1771 (2006).
6.C. A. van Iersel, H. J. de Koning, G. Draisma, W. P. T. M. Mali, E. Th. Scholten, K. Nackaerts, M. Prokop, J. Dik. F. Habbema, M. Oudkerk, and R. J. van Klaveren, “Risk-based selection from the general population in a screening trial: Selection criteria, recruitment and power for the Dutch-Belgian randomised lung cancer multi-slice CT screening trial (NELSON),” Int. J. Cancer 120(4), 868–874 (2007).
7.R. V. van Klaveren, M. Oudkerk, W. Mali, E. Scholten, K. Nackaerts, E. Thunnissen, and H. J. de Koning, “Baseline and second round results from the population based Dutch-Belgian randomized lung cancer screening trial (NELSON),” J. Clin. Oncol. 26, 1508 (2008).
9.C. L. Novak et al., “Inter-observer variations on interpretation of multi-slice CT lung cancer screening studies, and the implications for computer-aided diagnosis,” Proc. SPIE 4684, 68–79 (2002).
11.D. Wormanns, K. Ludwig, F. Beyer, W. Heindel, and S. Diederich, “Detection of pulmonary nodules at multirow-detector CT: Effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT,” Eur. Radiol. 15(1), 14–22 (2005).
12.J. K. Leader et al., “Pulmonary nodule detection with low-dose CT of the lung: Agreement among radiologists,” AJR, Am. J. Roentgenol. 185, 973–978 (2005).
13.F. Li, H. Arimura, K. Suzuki, J. Shiraishi, Q. Li, H. Abe, R. Engelmann, S. Done, H. MacMahon, and K. Doi, “Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization,” Radiology 237, 684–690 (2005).
14.G. D. Rubin, J. K. Lyo, D. S. Paik, A. J. Sherbondy, L. C. Chow, A. N. Leung, R. Mindelzun, P. K. Schraedley-Desmond, S. E. Zinck, D. P. Naidich, and S. Naipel, “Pulmonary nodules on multi-detector row CT scans: Performance comparison of radiologists and computer-aided detection,” Radiology 234, 274–283 (2005).
15.J. Lee, G. Gamsu, J. Czum, N. Wu, R. Johnson, and S. Chakrapani, “Lung nodule detection on chest CT: Evaluation of a computer aided detection (CAD) system,” Korean J. Radiol. 6, 89–93 (2005).
16.M. Das, G. Mhlenbruch, A. H. Mahnken, T. G. Flohr, L. Gndel, S. Stanzel, T. Kraus, R. W. Gnther, and J. E. Wildberger, “Small pulmonary nodules: Effect of two computer-aided detection systems on radiologist performance,” Radiology 241, 564–571 (2006).
17.B. Sahiner et al., “Effect of CAD on radiologists’ detection of lung nodules on thoracic CT scans: Analysis of an observer performance study by nodule size,” Acad. Radiol. 16(12), 1518–1530 (2009).
18.B. van Ginneken, S. G. Armato, B. de Hoop, S. van de Vorst, T. Duindam, M. Niemeijer, K. Murphy, A. M. R. Schilham, A. Retico, M. E. Fantacci, N. Camarlinghi, F. Bagagli, I. Gori, T. Hara, H. Fujita, G. Gargano, R. Belloti, F. D. Carlo, R. Megna, S. Tangaro, L. Bolanos, P. Cerello, S. C. Cheran, E. L. Torres, and M. Prokop, “Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study,” Med. Image Anal. 14(6), 707–722 (2010).
20.T. Lindeberg, “On scale selection for differential operators,” Proceedings of the Eighth Scandinavian Conference on Image Analysis, pp. 857–866, May 1993 (unpublished).
21.V. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, 1995).
22.V. Vapnik, Statistical Learning Theory (Wiley, New York, 1998).
23.J. W. Wallis, T. R. Miller, C. A. Lerner, and E. C. Kleerup, “Three-dimensional display in nuclear medicine,” IEEE Trans. Med. Imaging 8(4), 297–303 (1989).
24.A. Khotanzad, “Rotation invariant pattern recognition using Zernike moments,” Proceedings of the Ninth International Conference on Pattern Recognition, pp. 326–328, 1988 (unpublished).
25.S. Hu, E. A. Hoffman, and J. M. Reinhardt, “Automatic lung segmentation for accurate quantitation of volumetric x-ray CT images,” IEEE Trans. Med. Imaging 20, 490–498 (2001).
27.T. W. Ridler and S. Calvard, “Picture thresholding using an iterative selection method,” IEEE Trans. Syst. Man Cybern. SMC-8, 630–632 (1978).
29.D. Reisfeld, H. Wolfson, and Y. Yeshurun, “Context free attentional operators: The generalized symmetry transform,” Int. J. Comput. Vis. 14, 119–130 (1995), special issue on qualitative vision.
30.V. Di Gesù and C. Valenti, “The discrete symmetry transform in computer vision,” Technical Report No. DMA 011 95 (Palermo University, 1995).
31.P. D. Kovesi, “Symmetry and asymmetry from local phase,” Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence, 1997 (unpublished).
32.A. Witkin, “Scale-space filtering: A new approach to multi-scale description,” Proceedings of the International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, 1983 (Morgan-Kaufmann, Palo Alto, CA, 1983).
34.A. Riccardi, “A new computer aided system for the detection of nodules in lung CT exams,” Ph.D. thesis, University of Bologna, 2006.
35.F. V. Coakley, M. D. Cohen, M. S. Johnson, R. Gonin, and M. P. Hanna, “Maximum intensity projection images in the detection of simulated pulmonary nodules by spiral CT,” Br. J. Radiol. 71(842), 135–140 (1998).
36.J. F. Gruden, S. Ouanounou, S. Tigges, S. D. Norris, and T. S. Klausner, “Incremental benefit of maximum-intensity-projection images on observer detection of small pulmonary nodules revealed by multidetector CT,” AJR, Am. J. Roentgenol. 179(1), 149–157 (2002).
38.R. Opfer and R. Wiemker, “Performance analysis for computer-aided lung nodule detection on LIDC data,” Proc. SPIE 6515, 65151C–9 (2007).
39.S. Yamamoto, M. Matsumoto, Y. Tateno, T. Iinuma, and T. Matsumoto, “Quoit filter: A new filter based on mathematical morphology to extract the isolated shadow, and its application to automatic detection of lung cancer in x-ray CT,” Proceedings of the 13th International Conference on Pattern Recognition, Vol. 2, pp. 3–7, 1996 (unpublished).
40.Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, “Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,” IEEE Trans. Med. Imaging 20, 595–604 (2001).
41.M. N. Gurcan, B. Sahiner, N. Petrick, H. -P. Chan, E. A. Kazerooni, P. N. Cascade, and L. Hadjiiski, “Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system,” Med. Phys. 29, 2552–2558 (2002).
42.R. Wiemker, P. Rogalla, A. Zwartkruis, and T. Blaffer, “Computer aided lung nodule detection on high resolution CT data,” Proc. SPIE 4684, 677–688 (2002).
43.D. Kim, J. Kim, S. Noh, and J. Park “Pulmonary nodule detection using chest CT images,” Acta Radiol. 44, 252–257 (2003).
44.K. Suzuki, S. G. Armato III, F. Li, S. Sone, and K. Doi, “Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography,” Med. Phys. 30, 1602–1617 (2003).
45.K. T. Bae, J. Kim, Y. Na, K. G. Kim, and J. Kim, “Pulmonary nodules: Automated detection on CT images with morphologic matching algorithm—Preliminary results,” Radiology 236, 286–293 (2005).
46.M. S. Brown, J. G. Goldin, S. Rogers, H. J. Kim, R. D. Suh, M. F. McNitt-Gray, S. K. Shah, D. Truong, K. Brown, J. W. Sayre, D. W. Gjertson, P. Batra, and D. R. Aberle, “Computer-aided lung nodule detection in CT: Results of large-scale observer test,” Acad. Radiol. 12, 681–686 (2005).
47.Z. Ge, B. Sahiner, H. Chan, L. M. Hadjiiski, P. N. Cascade, N. Bogot, E. A. Kazerooni, J. Wei, and C. Zhou, “Computer-aided detection of lung nodules: False positive reduction using a 3D gradient field method and 3D ellipsoid fitting,” Med. Phys. 32, 2443–2454 (2005).
48.A. S. Roy, S. G. Armato III, A. Wilson, and K. Drukker, “Automated detection of lung nodules in CT scans: False positives reduction with the radial-gradient index,” Med. Phys. 33, 1133–1140 (2006).
49.B. Sahiner, L. M. Hadjiiski, H. Chan, C. Zhou, and J. Wei, “Computerized lung nodule detection on screening CT scans: Performance on juxta-pleural and internal nodules,” in Medical Imaging: Image Processing, San Diego, CA, 2006, edited by J. M. Reinhardt and J. P. Pluim (SPIE, Bellingham, WA, 2006), Vol. 6144, pp. 61445S–6.
50.B. Sahiner et al., “The effect of nodule segmentation on the accuracy of computerized lung nodule detection on CT scans: Comparison on a data set annotated by multiple radiologists,” Proc. SPIE 6514, 65140L–7 (2007).
51.R. Bellotti et al., “A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model,” Med. Phys. 34, 4901–4910 (2007).
52.I. Gori et al., “Multi-scale analysis of lung computed tomography images,” J. Sci. Instrum. 2, P09007–1P09007–16 (2007).
53.K. Murphy, A. Schilham, H. Gietema, M. Prokop, and B. van Ginneken, “Automated detection of pulmonary nodules from low-dose computed tomography scans using a two-stage classification system based on local image features,” Proc. SPIE 6514, 651410, 2007.
54.Q. Li, F. Li, and K. Doi, “Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier,” Acad. Radiol. 15(2), 165–175 (2008).
57.T. Messay, R. C. Hardie, and S. K. Rogers, “A new computationally efficient CAD system for pulmonary nodule detection in CT imagery,” Med. Image Anal. 14(3), 390–406 (2010).
58.R. C. Gonzales and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, Upper Saddle River, NJ, 2008).
59.E. B. Wilson, “Probable inference, the law of succession, and statistical inference,” J. Am. Stat. Assoc. 22, 209–212 (1927).
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