Classification of normal, benign and malignant tissues using co-occurrence matrix and bayesian neural network in mammographic images

Proceedings of the Ninth Brazilian Symposium on Neural Networks, SBRN'06

Volumn , Issue , 2006, Pages 24-29

  Martins, Leonardo De O ^a   Dos Santos, Alcione M ^b   Silva, Aristófanes C ^a   Paiva, Anselmo C ^a  

^a FEDERAL UNIVERSITY OF MARANHÃO   (Brazil)

^b Hospital Universitario   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; IMAGE ANALYSIS; MATRIX ALGEBRA; MEDICAL IMAGING; TISSUE;

BAYESIAN NEURAL NETWORKS; FORWARD SELECTION TECHNIQUES;

NEURAL NETWORKS;

EID: 34848908371     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SBRN.2006.14     Document Type: Conference Paper

References (23)

1. A. C. S. (AMS). Learn about breast cancer, 2006. Available at http://www.cancer.org.
2. a edition, 1985.
3. a edition, 1996.
4. W. L. Buntine and A. S. Weigend. Bayesian backpropagation. Complex Systems, 5:603-643, 1991.
5. A. K. Christoyianni, E. Dermatas, and G. Kokkinakis. Computer aided diagnosis of breast cancer in digitized mammograms. Computerized Medical Imaging and Graphics, 26:309-319, 2002.
6. R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. Wiley-Interscience Publication, New York, 1973.
7. D. Gamerman. Markov chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Chapman and Hall, London, 1997.
8. S. Geman and D. Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6):721-741, 1984.
9. M. Ghosh, T. Maiti, S. Chakraborty, and A. Tewari. Hierarchical bayesian neural networks: An application to prostate cancer study. Journal of the American Statistical Association, 99(467): 601-608, 2004.
10. W. Gilks and P. Wild. Adaptive rejection sampling for Gibbs sampling. Journal of the Royal Statistical Society Series C (Applied Statistics), (41):337-348, 1992.
11. R. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, SMC 3(6):610-621, November 1973.
12. W. K. Hastings. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 1970.
13. A. K. Jain. Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs, NJ, USA, 1989.
14. Y. Jiang, R. M. Nishikawa, R. A. Schmidt, A. Y. Toledano, and K. Doi. Potential of computer-aided diagnosis to reduce variability in radiologists interpretations of mammograms depicting microcalcifications. Radiology, 220:787-794, 2001.
15. K.Thangavel and M.Karnan. Computer aided diagnosis in digital mammograms: Detection of microcalcifications by meta heuristic algorithms. GVIP, 5, 2005.
16. D. J. C. Mackay. Bayesian Methods for Adaptive Models. Tese de doutorado, California Institute of Technology, 1991.
17. D. J. C. Mackay. A practical Bayesian framework for backpropagation networks. Neural Computation, 4(3):448-472, 1992.
18. N. C. I. (NCI). Cancer stat fact sheets: Cancer of the breast, 2006. Available at http://seer.cancer.gov/statfacts/html/breast.html.
19. R. M. Neal. Bayesian Learning for Neural Networks. Springer-Verlag, New York, 1996.
20. D. Rios Insua and P. Müller. Feedforward neural networks for nonparametric regression. In D. Dey, P. Müller, and D. Sinha, editors, Practical Nonparametric and Semiparametric Bayesian Statistics, pages 181-193. Springer-Verlag, New York, 1998.
21. D. J. Spiegelhalter, A. Thomas, N. Best, and D. Lunn. WinBUGS manual version 1.4. Technical report, MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, 2003.
22. J. Suckling, J. Parker, D. Dance, S. Astley, I. Hutt, C. Boggis, and et al. The mammographic images analysis society digital mammogram database. Exerpta Medical, 1069:375-378, 1994.
23. P. Zhang, B. Verma, and K. Kumar. Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recognition Letters, 26:909-919, 2005.