Detection and classification of microcalcification clusters in mammograms using evolutionary neural networks

Studies in Computational Intelligence

Volumn 107, Issue , 2008, Pages 151-180

  Hernández Cisneros, Rolando R ^a   Terashima Marín, Hugo ^a   Conant Pablos, Santiago E ^a  

^a SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 50449110222     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-77662-8_7     Document Type: Article

References (78)

1. American Cancer Society (ACS): Cancer facts and figures 2007. American Cancer Society, Inc., Clifton Road, NE, Atlanta, GA (2007).
2. Anttinen, I., Pamilo, M., Soiva, M., Roiha, M.: Double reading of mammography screening films: One radiologist or two? Clin. Radiol. 48 (1993) 414-421.
3. Balakrishnan, K., Honavar, V.: Evolutionary design of neural architectures. A preliminary taxonomy and guide to literature. Technical Report CS TR 95-01, Department of Computer Sciences, Iowa State University (1995).
4. Bankman, I. N., Tsai, J., Kim, D. W. et al.: Detection of microcalcification clusters using neural networks. In Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Volume 16 (1994) 590-591.
5. Bazzani, A., Bollini, D., Brancaccio, R. et al.: System for automatic detection of clustered microcalcifications in digital mammograms. International Journal of Modern Physics, 11(5) (2000) 901-912.
6. Betal, D., Roberts, N.,Whitehouse, G. H.: Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology. The British Journal of Radiology, 70 (1997) 903-917.
7. Bocchi, L., Coppini, G., Nori, J., Valli, G.: Detection of single and clustered microcalcifications in mammograms using fractal models and neural networks. Med. Eng. Phys, 26(4) (2004) 303-312.
8. Bowcock, A. M.: Breast cancer: Molecular genetics, pathogenesis, and therapeutics (Contemporary cancer research). (1st Edition). Humana Press (1999) 582 pp.
9. Cantú-Paz, E., Kamath, C.: Evolving neural networks for the classification of galaxies. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2002, San Francisco, CA, USA (2002) 1019-1026.
10. Caputo, B., La Torre, E., Gigante, G. E.: Microcalcification detection using a kernel bayes classifier. in Proceedings of the Third International Symposium on Medical Data Analysis, ISMDA 2002, Rome, Italy, Springer, (2002) 20-31.
11. Cheng, H. D., Lui, Y.M., Freimanis, R. I.: A novel approach to microcalcification detection using fuzzy logic technique. IEEE Transactions on Medical Imaging, 17(3) (1998) 442-450.
12. Ciatto, S., Del Turo, M. R., Risso, S. et al.: Comparison of standard reading and computer aided detection (CAD) on a national proficiency test of screening mammography. European Journal of Radiology, 45 (2) (2003) 135-138.
13. Comer, M. L., Liu, S., Delp, E. J.: Statistical segmentation of mammograms. In 3rd International Workshop on Digital Mammography, Chicago, IL (1996) 475-478.
14. Dengler, R., Behrens, S., Desaga, J. F.: Segmentation of microcalcifications in mammograms. IEEE Trans. Med. Imag., 12(4) (1993).
15. Diyana, W. M., Larcher, J., Besar, R.: A comparison of clustered microcalcifications automated detection methods in digital mammograms. IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2003, Hong Kong (2003).
16. Elmore, J. G., Miglioretti, D. L., Reisch, L. M. et al.: Screening mammograms by community radiologists: Variability in false-positive rates. Journal of the National Cancer Institute, 94(18) (2002) 1373-1380.
17. El-Naqa, I., Yang, Y., Wernick, M. N. et al.: A support vector machine approach for detection of microcalcifications. IEEE Transactions on Medical Imaging, 21(12) (2002) 1552-1563.
18. Fogel, D. B., Wasson III, E. C., Boughton, E. M., Porto, V. W.: Evolving artificial neural networks for screening features from mammograms. Artificial Intelligence in Medicine, Volume 14(1998) 317-326.
19. Führ, H., Treiber, O., Wanninger, F.: Cluster-oriented detection of microcalcifications in simulated low-dose mammography. Bildverarbeitung für die Medizin (2003) 96-100.
20. Ganott, M. A., Harris, K. M., Klaman, H. M., Keeling, T. L.: Analysis of false-negative cancer cases identified with a mammography audit. The Breast Journal, 5(3) (1999) 166.
21. Gavrielides, M. A.: A computer aid for the detection of suspicious microcalcification clusters in digitized mammograms. Master's thesis, Duke University (2002).
22. Goldberg, D. E.: Genetic algorithms in search, optimization and machine learning. Kluwer Academic Publishers, Boston, MA (1989).
23. Greene, F. L., Page, D. L., Fleming, I. D. et al.: AJCC Cancer Staging Manual (6th Edition). Springer (2002) 435.
24. Gulsrud, T. O.: Analysis of mammographic microcalcifications using a computationally efficient filter bank. Technical Report, Department of Electrical and Computer Engineering, Stavanger University College (2001).
25. Gupta, L., Srinath, M. D.: Contour sequence moments for the classification of closed planar shapes. Pattern Recognition, 20(3) (1987) 267-272.
26. Gurcan, M. N., Chan, H.-P., Sahiner, B. et al.: Optimal neural network architecture selection: Improvement in computerized detection of microcalcifications, Academic Radiology 9(4) (2002) 420-429.
27. Halkiotis, S., Mantas, J., Botsis, T.: Computer-aided detection of clustered microcalcifications in digital mammograms. In 5th European Conference in Systemic Science, Crete (2002).
28. Haykin, S.: Neural networks: A comprehensive foundation. Prentice Hall (1999).
29. Hernández-Cisneros, R. R., Terashima-Marín, H., Conant-Pablos, S. E.: Comparison of class separability, forward sequential search and genetic algorithms for feature selection in the classification of individual and clustered microcalcifications in digital mammograms. International Conference in Image Analysis and Recognition ICIAR 2007, Springer Verlag, Montreal, Canada (2007) 911-922.
30. Hernández-Cisneros, R. R., Terashima-Marín, H.: Classification of individual and clustered microcalcifications in digital mammograms using evolutionary neural networks, MICAI 2006, Springer Verlag, Apizaco, Tlaxcala, Mexico (2006) 1200-1210.
31. Hernández-Cisneros, R. R., Terashima-Marín, H.: Evolutionary neural networks applied to the classification of microcalcification clusters in digital mammograms. In Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada (2006) 2459-2466.
32. Hernández-Cisneros, R. R., Terashima-Marín, H.: Feature selection for the classification of microcalcification clusters in digital mammograms using genetic algorithms. GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation MedGEC 2006, Seattle, USA (2006).
33. Holland, J. H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975).
34. Hojjatoleslami, A., Sardo, L., Kittler, J.: An RBF based classifier for the detection of microcalcifications in mammograms with outlier rejection capability. International Conference on Neural Networks, Volume 3 (1997) 1379-1384.
35. Hong, B.-W., Brady, M.: Segmentation of mammograms in topographic approach. In IEE International Conference on Visual Information Engineering, Guildford, UK (2003).
36. Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Information Theory, Vol. IT-8 (1962) 179-187.
37. Ibrahim, N., Fujita, H., Hara, T., Endo, T.: Automated detection of clustered microcalcifications on mammograms: CAD system application to MIAS database. Physics in Medicine and Biology, 42(12) (1997) 2577-2589.
38. Kim, J. K., Park, H. W.: Statistical textural features for detection of microcalcifications in digital mammograms. IEEE Transactions on Medical Imaging, 18(3) (1999) 231-238.
39. Kim, J. K., Park, J. M., Song, S. S., Park, H. W.: Detection of clustered clustered microcalcifications on mammograms using surrounding region dependence method and artificial neural network. J. VLSI Signal Process., Volume 18 (1998) 251-262.
40. Kozlov, A., Koller, D.: Nonuniform dynamic discretization in hybrid networks. In Proceedings of the 13th Annual Conference of Uncertainty in AI (UAI), Providence, Rhode Island, USA (2003) 314-325.
41. Lasztovicza, K., Pataki, B., Székely, N., Tóth, N.: Neural network based microcalcification detection in a mammographic CAD system. In Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2003, Lviv, Ukraine (2003).
42. Lefebvre, F., Benali, H., Gilles, R. et al. A fractal approach to the segmentation of microcalcifications in digital mammograms. Med. Phys., 22(4) (1995) 381-390.
43. Lemaur, G., Drouiche, K., DeConinck, J.: Highly regular wavelets for the detection of clustered microcalcifications in mammograms. IEEE Transactions on Medical Imaging, 25(3) (2003) 393-401.
44. Li, S., Hara, T., Hatanaka, Y. et al.: Performance evaluation of a CAD system for detecting masses on mammograms by using the MIAS database. Medical Imaging and Information Science, 18(3) (2001) 144-153.
45. Li, H., Liu, K. J., Lo, S. C.: Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms. IEEE Trans. Med. Imaging, 16(6) (1997) 785-798.
46. Linguraru, M. G., Brady, J. M., Yam, M.: Detection of microcalcifications using SMF. In 6th International Workshop of Digital Mammography (Lecture Notes in Computer Science), Springer Verlag (2002).
47. Liu, J., Hwang, W., Chen, M.: Estimation of 2-D noisy fractional brownian movement and its applications using wavelets. IEEE Transactions on Image Processing, 9(8) (2000) 1407.
48. McGarry, G.: Performance of the generalized gaussian distribution for detection of calcifications in mammographic images. Signal Processing Research Centre, Queensland University of Technology, Brisbane, Australia (1999).
49. Melloul, M., Joskowicz, L.: Segmentation of microcalcifications in X-ray mammograms using entropy thresholding. In Computer Assisted Radiology and Surgery, CARS 2002, Springer (2002).
50. Nishikawa, R. M.: Computer aided detection of clustered microcalcifications: An improved method for grouping detected signals. Medical Physics, 20(6) (1993) 1661-1666.
51. Norhayati, I., Hiroshi, F., Takeshi, H., Tokiko, E. Automated detection of clustered microcalcifications on mammograms: CAD system application to MIAS database. Phys. Med. Biol., 42 (1997) 2577-2589.
52. Ochoa, E. M.: Clustered microcalcification detection using optimized difference of gaussians. Master's Thesis, Air Force Institute of Tech. Wright-Patterson (1996).
53. Oporto-Díaz, S., Hernández-Cisneros, R. R., Terashima-Marín H.: Detection of microcalcification clusters in mammograms using a difference of optimized gaussian filters. In Proceedings of the International Conference in Image Analysis and Recognition ICIAR 2005, Toronto, Canada (2005) 998-1005.
54. Papadopoulossa, A., Fotiadisb, D. I., Likasb, A.: An automatic microcalcification detection system based on a hybrid neural network classifier. Artificial intelligence in Medicine, 25 (2002) 149-167.
55. Papik, K., Molnar, B., Schaefer, R. et al.: Application of neural networks in medicine - a review. Med. Sci. Monit., 4(3) (1998) 538-546.
56. Patrocinio, A. C., Schiabel, H., Benatti, R. H. et al.: Investigation of clustered microcalcification features for an automated classifier as part of a mammography CAD scheme. Engineering in Medicine and Biology Society. Proceedings of the 22nd Annual International Conference of the IEEE Volume 2 (2000) 1203-1205.
57. Penny, W., Frost, D.: Neural networks in clinical medicine. Med. Decis. Making, 16 (1996) 386-398.
58. Polakowski, W. E., Cournoyer, D. A., Rogers, S. K. et al.: Computer-aided breast cancer detection and diagnosis of masses using difference of gaussians and derivative-based feature saliency. IEEE Transactions on Medical Imaging, 16(6) (1997) 811-819.
59. Ricketts, I. W., Cairns, A. Y., Folkes, D. et al.: The automated detection of clusters of microcalcifications. In IEE Colloquium: Applications of Image Processing in Mass Health Screening, Digest No. 1992/056, London (1992) 13-15.
60. Sajda, P., Spence, C., Pearson, J.: Learning contextual relationships in mammograms using a hierarchical pyramid neural network. IEEE Transactions on Medical Imaging, 21(3) (2002) 239-250.
61. Salfity, M. F., Kaufmann, G. H., Granitto, P., Ceccatto, H. A.: Automated detection and classification of clustered microcalcifications using morphological filtering and statistical techniques. In 5th International Workshop on Digital Mammography, Toronto, Canada (2000).
62. Sehad, S., Desarnaud, S., Strauss, A.: Artificial neural classification of clustered microcalcifications on digitized mammograms. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Volume 5 (1997) 4217-4222.
63. Skinner, A., Broughton, J. Q.: Neural networks in computational material science: Traning algorithms. Modeling and Simulation in Material Science and Engineering, 3 (1995) 371-390.
64. Sordo, M.: Introduction to neural networks in healthcare [WWW document]. Open Clinical (2002) URL: http://www.openclinical.org/docs/int/ neuralnetworks011.pdf
65. Strausz, G., Horváth, G., Pataki, B. et al.: Intelligent solution for mammography image diagnosis. In Engineering Application of Neural Networks Conference EANN 2003, Malaga, Spain (2003).
66. Suckling, J., Parker, J., Dance, D. et al.: The Mammographic Images Analysis Society digital mammogram database. Exerpta Medica International Congress Series, 1069 (1994) 375-378. URL: http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html
67. Thurfjell, E. L., Lernevall, K. A., Taube, A. A. S.: Benefit of independent double reading in a population-based mammography screening program. Radiology, 191 (1994) 241-244.
68. Treiber, O., Wanninger, F., Führ, H. et al.: An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography. GSF - National Research Center for Environment and Health (2002) 1-24.
69. Ustymowics, M., Nieniewski, M.: Clustering microcalcifications in mammograms by means of morphology based strategy. In Fourth IEEE Benelux Signal Processing Symposium, Hilvarenbeek, Netherlands (2004).
70. Verma, B.: A neural network based technique to locate and classify microcalcifications in digital mammograms. In Proceedings of the IEEE International Conference on Neural Networks, Volume 3 (1998) 1790-1793.
71. World Health Organization: Fact sheet No. 297: Cancer [WWW document]. (2006) URL: http://www.who.int/mediacentre/factsheets/fs297/en/index.html
72. Woods, K., Doss, C., Bowyer, K. et al.: A neural network approach to microcalcification detection. IEEE 1992 Nuclear Science Symposium and Medical Imaging Conference, Orlando, FL (1992) 1273-1275.
73. Wróblewska, A., Boninski, P., Przelaskowski, A., Kazubek, M.: Segmentation and feature extraction for reliable classification of microcalcifications in digital mammograms, Opto Electronics Review, 11(3) (2003) 227-235.
74. Yao, X.: Evolution of connectionist networks. In Preprints Int. Symp. AI, Reasoning and Creativity, Queensland, Australia, Griffith Univ. (1991) 49-52.
75. Yao, X.: Evolving artificial neural networks. In Proceedings of the IEEE, 87(9) (1999) 1423-1447.
76. Yu, S., Guan, L.: A CAD system for the automated detection of clustered microcalcifications in digitized mammogram films. IEEE Transactions on Medical Imaging, 19(2) (1999) 115-126.
77. Zhao, D., Shridhar, M., Daut, D. G.: Morphology on detection of calcifications in mammograms. In IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 92, Volume 3 (1992) 129-132.
78. Zheng, B., QianM W., Clarke, L. P.: Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications. IEEE Trans. Med. Imag., 15(5) (1996) 589-597.