An improved medical decision support system to identify the breast cancer using mammogram

Journal of Medical Systems

Volumn 36, Issue 1, 2012, Pages 79-91

  Suganthi, Muthusamy ^a   Madheswaran, Muthusamy ^a  

^a MUTHAYAMMAL ENGINEERING COLLEGE   (India)

Author keywords

Back propagation network; Breast tumor; Feature extraction; Image denoising and enhancement; Mammogram; Stages

Indexed keywords

ARTICLE; BACK PROPAGATION; BREAST CANCER; CANCER STAGING; COMPUTER ASSISTED RADIOGRAPHY; GENETIC ALGORITHM; HUMAN; IMAGE ANALYSIS; MAMMOGRAPHY; MEDICAL DECISION MAKING; MEDICAL EXPERT; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SIGNAL NOISE RATIO; TUMOR GROWTH; TUMOR VOLUME; ALGORITHM; ARTIFICIAL NEURAL NETWORK; BREAST TUMOR; DECISION SUPPORT SYSTEM; FEMALE; IMAGE PROCESSING; PATHOLOGY; RADIOGRAPHY;

ALGORITHMS; BREAST NEOPLASMS; DECISION MAKING, COMPUTER-ASSISTED; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAMMOGRAPHY; NEOPLASM STAGING; NEURAL NETWORKS (COMPUTER);

EID: 84860254276     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9448-5     Document Type: Article

References (29)

1. Cheng, H. D., Shi, X. J., Min, R., Hu, L. M., Cai, X. P., and Du, H. N., Approaches for automated detection and classification of masses in mammograms. Pattern Recogn. 39:646-668, 2006. (Pubitemid 43199461)
2. Rangayyan, R. M., Xu, J., Elnaqa, I., and Yang, Y., Jr., Computeraided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans. Inf. Technol. Biomed. 13 (2):236-251, 2009.
3. Yang, S.-C., Wang, C.-M., Chung, Y.-N., Hsu, G.-C., Lee, S.-K., Chung, P.-C., and Chang, C., A computer-aided system for mass detection and classification in digitized mammograms. Biomed Eng. Appl. Basis Commun. 17:215-228, 2005. (Pubitemid 41809712)
4. Tulio, C. S. S. A., and Rangayyan, R. M. Classification of breast masses in mammograms using neural networks with shape, edge sharpness, and texture features. J Electron Imaging 15(1), 2006.
5. Rangayyan, R. M., da Silva, L. A., and Del Moral Hernandez, E. Classification of breast masses using a committee machine of artificial neural networks. J. Electro. Imaging 17(1), 2008.
6. Alolfe, M. A., Youssef, A.-B. M., Kadah, Y. M., and Mohamed, A. S.. Development of a computer aided classification system for Cancer detection from digital mammograms. 25th National Radio Science Conference, Faculty of Engineering, Tanta Univ., Egypt, 2008.
7. Drukker, K., Sennett, C. A., and Giger, M. L., Automated method for improving system performance of computer-aided diagnosis in breast ultrasound. IEEE Trans. Med. Imaging 28(1):122-128, 2009.
8. Zhang, G., and Zhao, H.. A CAD system in mammography using ANN. International conference on biomedical engineering and informatics, 2008.
9. Hadhoud, M., Amin, M., and Dabbour, W.. Detection of breast cancer tumor algorithm using mathematical morphology and wavelet analysis. GVIP 05 Conference, CICC, Cairo, Egypt, 2005.
10. Giger, M. L., Nishikawa, R. M., Doi, K., Vyborny, C. J., and Schmidt, R. A., Computer-aided detection of clustered microcalcifications on digital mammograms. Med. Biol. Eng. Comput. 33:174-178, 1995.
11. Huo, Z., Giger, M. L., Vyborny, C. J., Wolverton, D. E., Schmidt, R. A., and Doi, K., Automated computerized classification of malignant and benign mass lesions on digitized mammograms. Acad. Radiol. 15:155-168, 1998. (Pubitemid 28372697)
12. de Oliveira Martins, L., Silva, A. C., de Paiva, A. C., and Gattass, M., Detection of breast masses in mammogram images using growing neural gas algorithm and Ripley's K function. J. Sign. Process. Syst., Springer 55:77-90, 2009.
13. Yoshida, H., Doi, K., Nishikawa, R., Giger, M., and Schmidt, R., An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms. Acad. Radiol. 3:621-627, 1996.
14. Bettahar, S., and Stambouli, A. B. Shock filter coupled to curvature diffusion for image denoising and Sharpening. Journal of image vision and computing, Vol. 26. Elsevier, pp. 1481-1489, 2008.
15. Suganthi, M., and Madheswaran. Mammogram image enhancement and denoising using shock filters. Proceedings of International Conference on Advanced Communication and Informatics, TPGIT, Vellor, India, 2009.
16. Alvarez, L., and Mazorra, L., Signal and image restoration using shock filters and anisotropic diffusion. SIAM J. Num. Anal. 31 (2):590-605, 1994.
17. Karahaliou, A., Skiadopoulos, S., Boniatis, I., Sakellaropoulous, P., Likaki, E., Panayiotakis, G., and Costaridou, L., Texture analysis of tissure sorrounding microcalcification on mammograms for breast cancer diagnosis. Br. J. Radiol. 80:648-656, 2007. (Pubitemid 47517810)
18. Haralick, R. M., Stastistical and structural approaches to texture. Proc. IEEE 67(5):786-804, 1979.
19. Jong Kook Kim, K., and Park, H. W., Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Trans. Med. Imaging 18(3):231-238, 1999.
20. Christodoulou, C. I., Pattichis, C. S., Pantziaris, M., and Nicolaides, A., Texture-based classification of atherosclerotic carotid plaques. IEEE Trans. Med. Imaging 22(7):902-911, 2003.
21. Raja, K. B., Madheswaran, M., and Thyagarajah, K., A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features. J. Med. Syst. 32:65-83, 2008.
22. Shen, C. L., Rangayyan, R. M., and Desautels, J. E. L., Application of shape analysis to mammographic calcifications. IEEE Trans. Med. Imaging 13:263-274, 1994.
23. Rangayyan, R. M., Mudigonda, N. R., and Desautels, J. E. L., Boundary modeling and shape analysis methods for classification of mammographic masses. Med. Biol. Eng. Comput. 38:487-496, 2000.
24. Bandyopadhyay, K. S., Pal, S. K., and Aruna, B., Multiobjective GAs, quantitative indices, and pattern classification. IEEE Trans. Syst. Man Cybern. B 34(5):2088-2099, 2004.
25. Roy, K., and Bhattacharya, P. Optimal features subset selection and classification for Iris recognition. J. Image Video Process. 1, Article ID 743103, pp. 20, doi:10.1155/2008/743103,2008.
26. Rajendra Achrya, U., Ng, E. Y. K., Chang, Y. H., Yang, J., and Kaw, G. J. L., Computer-based identification of breast cancer using digitized mammograms. J. Med. Syst. 38:499-507, 2008.
27. Moein, S., Monadjemi, S. A., and Moallem, P. A novel fuzzyneural based medical diagnosis system. Proceedings Of World Academy Of Science, Engineering And Technology, Vol. 27, ISSN 1307-6884, 2008.
28. Vlachogiannis, J. G., Hatziargyriou, N. D., and Lee, K. Y., Ant colony system-based algorithm for constrained load flow problem. IEEE Trans. Power. Syst. 20(3):1249-1250, 2005.
29. Alarcon, T., Byrne, H. M., and Maini, P. K., A multiple scale model for tumor growth. Multistage Model Simul. 3(2):440-475, 2005. (Pubitemid 40951980)