Neural networks for the classification of benign and malignant patterns in digital mammograms

Advances in Applied Artificial Intelligence

Volumn , Issue , 2006, Pages 251-272

  Verma, Brijesh ^a   Panchal, Rinku ^a  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 37649001780     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59140-827-7.ch009     Document Type: Chapter

References (61)

1. Aghdasi, F., Ward, R., & Palcic, B. (1994). Restoration on mammographic images in the presence of signal-dependent noise. In K. W. Bowyer & S. Astley (Eds.), State of the art in digital mammographic image analysis (Vol. 7(6), pp. 42-63). Singapore: World Scientific Publishing.
2. Anastasio, M., Yoshida, H., Nagel, R., Nishikawa, R., & Doi, K. (1998). A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered micro-calcifications in mammograms. Medical Physics, 25(9), 1613-1620.
3. th Workshop on Neural Network Applications - NEUREL, Belgrade, Yugoslavia.
4. Blake, L., & Merz, J. (1998). UCI Repository of Machine Learning Databases. Retrieved from the University of California Web site at http://www.ics.uci.edu/~mlearn/MLRepository.html.
5. th IEEE International Conference on Pattern Recognition, Quebec, Canada (Vol. 2, pp. 267-270).
6. Bovis, K., Singh, S, Fieldsend, J., & Pinder, C. (2000, July 24-27). Identification of masses in digital mammograms with MLP and RBF nets. Proceedings of the IEEE International Joint Conference on Neural Networks, Como, Italy (Vol. 1, pp. 1342-1348).
7. Chan, H., Sahiner, B., & Lam, K. (1998). Computerized analysis of mammographic microcalcifications in morphological and texture features space. Medical Physics, 25(10), 2007-2019.
8. Cheng, H., Cai, X., Chen, X., Hu, L., & Lou, X. (2003). Computer-aided detection and classification of micro-calcifications in mammograms: A survey. Pattern Recognition, 36, 2967-2991.
9. Chitre Y., Dhawan, A. P., & Moskowitz, M. (1993). Artificial neural network based classification of mammographic micro-calcifications using image structure features. International Journal of Pattern Recognition and Artificial Intelligence, 7, 1377-1401.
10. Christoyianni, L., Dermatas, E., & Kokkinakis, G. (1999, September 5-8). Neural classification of abnormal tissue in digital mammography using statistical features of the texture. Proceedings of the IEEE International Conference on Electronics Circuits and Systems, Pafos, Cyprus (Vol. 1, pp. 117-120).
11. Christoyianni, L., Dermatas, E., & Kokkinakis, G. (2000). Fast detection of masses in computer-aided mammography. IEEE Signal Processing Magazine, 17, 54-64.
12. st Canadian Medical and Biological Engineering Conference, Montreal, Canada (pp. 535-536).
13. Dhawan, A., Chitre, Y., & Moskowitz, M. (1993). Artificial neural network based classification of mammographic micro-calcifications using image structure features. In R. S. Acharya & D. B. Goldgof (Eds.), Biomedical Image Processing and Biomedical Visualization - Proceedings of SPIE (the International Society of Optical Engineering) (Vol. 1905, pp. 820-831).
14. rd International Workshop on Digital Mammography, Chicago (pp. 329-334).
15. Erich, S., & Ferdinand, S. (2000). Computer aided diagnosis of clustered micro-calcifications using artificial neural nets. Journal of Computing and Information Technology, 8, 151-160.
16. Gavrielides, M. A., Lo, J. Y. Jr., & Floyd, C. E. (2002). Parameter optimization of a computeraided diagnosis scheme for the segmentation of micro-calcification clusters in mammograms. Medical Physics, 29, 475-483.
17. Gonzalez, R., & Woods, R. (1993). Digital image processing. Reading, MA: Addison Wesley.
18. Gurcan, M., Chan, H., Sahiner, B., Hadjiiski, L., Petrick, N., & Helvie, M. (2002). Optimal neural network architecture selection: Improvement in computerized detection of micro-calcifications. Academic Radiology, 9, 420-429.
19. Gurcan, M., Sahiner, B., Chan, H., Hadjiiski, L., & Petrick, N. (2001). Selection of an optimal neural network architecture for computer-aided detection of micro-calcifications -Comparison of automated optimization techniques. Medical Physics, 28, 1937-1948.
20. th International Workshop on Digital Mammography, Toronto, Canada. Madison, WI: Medical Physics Publishing.
21. Jain, A. (1995). Fundamentals of digital image processing. Upper Saddle River, NJ: Prentice-Hall.
22. th International Conference of the IEEE/Engineering in Medicine and Biology Society (pp. 521-523).
23. Karssemeijer, N. (1994). Adaptive noise equalization and recognition of micro-calcification clusters in mammograms. In S. Astley (Eds.), State of the art of digital mammographic image analysis (Vol. 9(8), pp. 148-166). Singapore: World Scientific Publishing Series in Machine Perception and Artificial Intelligence.
24. Karssemeijer, M., Thijssen, M., Hendriks, J., & van Erning, L. (Eds.). (1998). Digital mammography (pp. 237-244). Nijmegen: Kluwer Academic Publishers.
25. Khuwaja, G. A., & Abu-Rezq, A. N. (2004). Bi-model breast cancer classification system. Pattern Analysis Applications, 7, 235-242.
26. Kim, J. K., & Park, H. W. (1999). Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Transactions on Medical Imaging, 18, 231-238.
27. Kocur, C., Gogers, S., Myers, L., Burns, T., Kabrisky, M., HoImeister, J., Baver, K., & Steppe, J. (1996). Using neural networks to select wavelet features for breast cancer diagnosis. IEEE Engineering in Medicine and Biology Magazine, 15(3), 95-102.
28. nd ed.). Philadelphia: Lippincott-Raven.
29. th International Workshop on Digital Mammography - IWDM2000, Toronto, Canada (p. 52). Madison, WI: Medical Physics Publishing.
30. Lee, Y., & Tsai, D. (2004). Computerized classification of micro-calcifications on mammograms using fuzzy logic and genetic algorithm. In M. Sonka & J. M. Fitzpatrick (Eds.), Medical Imaging - Proceedings of SPIE (Vol. 5370, pp. 952-959).
31. Lo, J. Y., Gavrielides M., Markey M. K., & Esneck J. L. (2003). Computer-aided classification of breast micro-calcification clusters: Merging of features from image processing and radiologists. In M. Sonka & J. M. Fitzpatrick (Eds.), Medical Imaging - Proceedings of SPIE (Vol. 5032, pp. 882-889).
32. Lo, J. Y., Land, W. H., & Morrison, C. T. (1999, July 10-16). Application of evolutionary programming and probabilistic neural networks to breast cancer diagnosis. Proceedings of the IEEE International Joint Conference on Neural Networks, Washington, DC (Vol. 5, pp. 3712-3716).
33. Lo, J. Y., Land, W. H., & Morrison, C. T. (2000). Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. In K. M. Hanson (Ed.), Medical Imaging, Image Processing - Proceedings of SPIE (Vol. 3979, pp. 153-158).
34. Markey, M. K., Lo, J. Y., & Floyd, C. E. (2002). Differences between computer-aided diagnosis of breast masses and that of calcifications. Radiology, 223, 489-493.
35. Markey, M., Lo, J., Tourassi, G., & Floyd, C. (2003). Self-organizing map for cluster analysis of a breast cancer database. Artificial Intelligence in Medicine, 27, 113-127.
36. th International Workshop on Digital Mammography - IWDM2000, Toronto, Canada, June 2000 (pp. 576-581). Madison, WI: Medical Physics Publishing.
37. th International Workshop on Digital Mammography - IWDM2000, Toronto, Canada, June 2000, (p. 103). Madison, WI: Medical Physics Publishing.
38. Panchal, R., & Verma, B. (2005, August 1-4). Classification of breast abnormalities in digital mammograms using image and BI-RADS features in conjunction with neural network. In Proceedings of the IEEE International Joint Conference on Neural Networks, Montreal, Canada.
39. Papadopoulos A., Fotiadis D. I., & Likas, A. (2002). An automatic micro-calcification detection system based on a hybrid neural network classifier. Artificial Intelligence in Medicine, 25, 149-167.
40. nd Annual IEEE/Engineering in Medicine and Biology Society International Conference, Chicago (pp. 1203-1205).
41. th International Workshop on Digital Mammography - IWDM2000, Toronto, Canada, June 2000 (p. 104). Madison, WI: Medical Physics Publishing.
42. Petrick, N., Chan, H., Sahiner, B., Wei, D., Helvie, M., Goodsitt, M., & Adler, D. (1995, July 17-21). Automated detection of breast masses on digital mammograms using a convolution neural network for morphological and texture classification. Proceedings of the World Congress on Neural Networks, Washington, DC (Vol. 2, pp. 872-875).
43. Sahiner, B., Chan, H., Petrick, N., Helvie, M., & Goodsitt, M. (1998). Design of a highsensitivity classifier based on a genetic algorithm: Application to computer-aided diagnosis. Physics in Medicine and Biology, 43, 2853-2871.
44. nd IEEE International Conference on Image Processing, Washington, DC (pp. 3149-3151).
45. rd IEEE International Workshop on Digital Mammography, Chicago (pp. 287-290). Amsterdam, The Netherlands: Elsevier.
46. Shen, L., Rangayyan, R. M., & Desautels, J. E. L. (1993). Detection and classification of mammographic calcifications. International Journal of Pattern Recognition and Artificial Intelligence, 7, 1403-1416.
47. Sonka, M., & Fitzpatrick, J. (2000). Medical image processing and analysis. Bellingham, WA: SPIE Press.
48. Umbaugh, S. (1998). Computer vision and image processing: A practical approach using CVIPtools. Upper Saddle River, NJ: Prentice Hall.
49. Verma, B. (1998, May 4-9). A neural network based technique to locate and classify microcalcifications in digital mammograms. Proceedings of the IEEE International Joint Conference on Neural Networks, Anchorage, Alaska (Vol. 3, pp. 1790-1793).
50. Verma, B., & Zakos, J. (2001). A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Transactions on Information Technology in Biomedicine, 5, 46-54.
51. Wei, Q., Laurence, P., & Clark, L. (1994, November). Adaptive multistage nonlinear filtering and wavelet for medical image enhancement. Proceedings of the IEEE International Conference on Image Processing - ICIP, 13-16, Austin, TX (Vol. 3, pp. 711-715).
52. rd IEEE International Workshop on Digital Mammography, Chicago (pp. 283-286). Amsterdam, The Netherlands: Elsevier.
53. Woods, K., Doss, C., Bowyer, K., Solka, J., Priebe, C., & Kegelmeyer, W. (1993). Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography. International Journal of Pattern Recognition and Artificial Intelligence, 7(6), 1417-1436.
54. Wroblewska, A., Boninski, P., Przelaskowski, A., & Kazubek, M., (2003). Segmentation and feature extraction for reliable classification of micro-calcification in digital mammograms. Opto-Electronics Review, 11, 227-235.
55. Wu, Y. (1993). Application of neural networks in mammography. Radiology, 187, 81-87.
56. Wu, Y., He, J., Man, Y., & Arribas, J. I. (2004, July 25-29). Neural network fusion strategies for identifying breast masses. In Proceedings of the IEEE International Joint Conference on Neural Networks, Budapest, Hungary (Vol. 3, pp. 2437-2442).
57. Yin, F., Giger, M., Vyborny, C., Doi, K., & Schmidt, R. (1993). Comparison of bilateralsubtraction and single-image processing techniques in the computerised detection of mammographic masses. Investigative Radiology, 28(6), 473-481.
58. Yoon, J., Ro, Y., Kim, S., & Park, D. (2002). Contrast enhancement of mammography image using homomorphic filter in wavelet domain. IEICE Transactions on Information & Systems, E85-D(1), 298-303.
59. Yu, S., & Guan, L. (2000). A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Transactions on Medical Imaging, 19, 115-126.
60. Zhang, P., Verma, B., & Kumar, K. (2004, July 25-29). A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography. Proceedings of the IEEE International Joint Conference on Neural Networks, Budapest, Hungary (Vol. 3, pp. 2303-2309).
61. Zheng, B., Qian, W., & Clarke, L. (1994, June 27-July 2). Multistage neural network for pattern recognition in mammogram screening. Proceedings of the IEEE International Joint Conference on Neural Networks, Orlando, FL (pp. 3437-3447).