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Published in:   Vol. 3 Issue 1 Date of Publication:   June 2014

A review on data mining techniques for Digital Mammographic Analysis

S.Poonguzhali,Ananthi Sheshasaayee

Page(s):   33-37 ISSN:   2278-2397
DOI:   10.20894/IJDMTA.102.003.001.008 Publisher:   Integrated Intelligent Research (IIR)


  1. Acharya, U. R., Ng, E. Y. K., Chang, Y. H., Yang, J., and Kaw, G.J. L.,   "Computer-based identification of breast cancer sing digitized mammograms",   J. Med. Syst   ,Vol.32   ,Issue 6   ,2008
    View Artical

  2. Aha, D.W., and Bankert, R.L,   "A comparative evaluation of sequential feature selection algorithm",   In D. Fisher& j.-H.Lenz(Eds.), Artificial intellig   ,1996
    View Artical

  3. Almuallium, H., and Dietterich, T.G,   "Efficient algorithm for indentifying relevant features, In proceedings of the Ninth Canadian Confere",   Vancouver, Bc:Morgan Kaufmann   ,1992
    View Artical

  4. Bjurstam, N., Bjorneld, L., Warwick, J., Sala, E., Duffy, S. W.,Nystr?m,L., et al.,   "The Gothenburg breast screening trial   ,Vol.97   ,Issue 10   ,2003
    View Artical

  5. Brijesh, B,   "Novel network architecture and learning algorithm for the classification of mass abnormalities in di",   Artif.Intell. Med   ,Vol.42   ,Issue 1   ,2008
    View Artical

  6. C.Chen and G.Lee,   "Image segmentation using multiresolution wavelet analysis and expectation maximization (em) algorith",   International Journal of Imaging System and Techno   ,Vol.8   ,Issue 5   ,1997
    View Artical

  7. Carney, P. A., Miglioretti, D. L., Yankaskas, B. C., Kerlikowske,K.,Rosenberg, R., Rutter, C. M., et al,   "Individual and combinedeffects of age, breast density, and hormone replacement therapyuse on the acc",   Ann. Intern.Med   ,Vol.138   ,Issue 3   ,2003
    View Artical

  8. Chhatwal, J., Alagoz, O., Lindstrom, M. J., Kahn, C. E., Jr., Shaffer, K.A., and Burnside, E. S,   "A logistic regression model based on the national mammography database format to aid breast cancer d",   Am. J. Roentgenol   ,Vol.192   ,Issue 4   ,2009
    View Artical

  9. Christoyianni et al,   "Fast Detection of masses in computer-aided mammography",   IEEE signal Processing Magazine   ,2000
    View Artical

  10. de Oliveira Martins, L., Junior, G. B., Correa Silva, A., de Paiva, A. C.,and Gattass, M,   "Detection of masses in digital mammograms using Kmeans and support vector machine",   Electron.Lett.Comput. Vis. Image.Ana   ,Vol.8   ,Issue 2   ,2009
    View Artical

  11. DursunDelen, Glenn Walker, AmitKadam,   "Predicting breast cancer survivability comparison of three data mining methods
    View Artical

  12. H. D. Cheng,JuanShan,WenJu, YanhuiGuo,Ling Zhang,   "Automated breast cancer detection and classification using ultrasound images: A survey",   Journal Pattern Recognition   ,Vol.43   ,Issue 1   ,2010
    View Artical

  13. Heine, J. J., Deans, S. R., Cullers, D. K., Stauduhar, R., and Clarke, L.P,   "Multiresolution statistical analysis of high-resolution digital mammograms",   IEEE. Trans. Med. Imaging   ,Vol.5   ,Issue 16   ,1997
    View Artical

  14. Herron P.,   "Machine learning for medical Decision Support: Evaluating Diagnostic Performance of machine learning",   Data Mining: Spring,   ,2004
    View Artical

  15. I.Christiyanni et al,   "Fast detection of masses in computer aided mammography",   IEEE Signal processing Magazine   ,2000
    View Artical

  16. Jun Xu and Jinshan Tang,   "Detection of clustered microcalcifications using an improved texture based approach for computer aid
    View Artical

  17. K. Bovis& S. Singh,   "Classification of mammographic breast density using a combined classifier paradigm",   In Med. Image Underst. Anal   ,2002
    View Artical

  18. Karssemeijer, N,   "Adaptive noise equalization and recognition of microcalcification clusters in mammograms",   Int. J. Pattern. Recog.Artificial.Intell   ,Vol.7   ,Issue 6   ,1993
    View Artical

  19. Kulkarni, A. D,   "Computer Vision and Fuzzy-Neural Systems.",   Prentice-Hall, Englewood-Cliffs   ,2001
    View Artical

  20. L.Yu and H.Liu,   "Feature selection for High-dimensional data: A fast correlation-based filter solution",   In Proc 12thIntconf on Machine Learning (ICML-03),   ,2003
    View Artical

  21. Li, Y., and Jiang, J.,   "Combination of SVM knowledge for microcalcification detection in digital mammograms",   Lect. Notes Comput. Sci   ,2004
    View Artical

  22. Lin Li , Peeter Ross , MaarjaKruusmaa , XiaosongZheng,   "A comparative study of ultrasound image segmentation algorithms for segmenting kidney tumors",   Proceedings of the 4th International Symposium on    ,2011
    View Artical

  23. M.Vasantha, DR.V.subbiahbharathi,R.Dhamodharan,   "Medical Image Feature, Extraction, Selection And Classification",   International Journal of Engineering Science and T   ,Vol.2   ,Issue 6   ,2010
    View Artical

  24. Maria-LuizaAntonie, OsmarR.Zaiane,   "Alexandrucoman applications of Data Mining Techniques for medical Image Classification",   Proceedings of the Second International Workshop o   ,2011
    View Artical

  25. N. Scales, C. Herry, M. Frize,   "Automated Image Segmentation for Breast Analysis Using Infrared Images",   Proceedings of the 26th Annual International Confe   ,2004
    View Artical

  26. Nikolaos Pagonis, DionisisCavouras, Kostas Sidiropoulos,   "George Sakellaropoulos and George Nikiforidis IMPROVING THE CLASSIFICATION ACCURACY OF COMPUTER AIDE",   e-Journal of Science & Technology
    View Artical

  27. Nystrom, L., Andersson, I., Bjurstam, N., Frisell, J., Nordenskjold, B.,and Rutqvist, L. E,   "Long-term effects of mammography screening:updated overview of the Swedish randomisedtrials",   Lancet.   ,2002
    View Artical

  28. Oky Dwi Nurhayati, Thomas Sri Widodo, Adhi Susanto, Maesadji Tjokronagoro,   "First Order Statistical Feature for breast Cancer Detection using thermal images",   Proc. Of World Academy of Science,Engineering and    ,Vol.70   ,2010
    View Artical

  29. Pisano, E. D., Gatonis, C., Hendrick, E., Yaffe, M., Baum, J.K.,Acharyya, S., et al,   "Diagnostic erformance of digital versus film mammography for breast-cancer screening",   N. Engl. J. Med   ,2005
    View Artical

  30. Pisano, E. D., Gatonis, C., Hendrick, E., Yaffe, M., Baum, J. K.,Acharyya, S., et al.,   "Diagnostic accuracy of digital versus film mammography: exploratory analysis of selected population ",   Radiology   ,Vol.246   ,Issue 3   ,2008
    View Artical

  31. Pragati Kapoor, Ekta Bhayana, Dr. S.V.A.V Prasad,   "Real Time Intelligent Thermal Analysis Approach for Early Diagnosis of Breast Cancer",   2010 International Journal of Computer Application   ,Vol.1   ,Issue 5   ,2010
    View Artical

  32. Priebe, C. E., Lorey, R. A., Marchette, D. J., Solka, J. L., and Rogers, G.W,   "Nonparametric spatio-temporal change point analysis for early detection in mammography",   In: Gale, A. G., Astley, S. M., Dance, D.R., and C   ,1994
    View Artical

  33. Qian, W., Sunden, P., Sjostrom, H., Fenger-Krog, H., and Brodin, U,   "Comparison of image quality for different digital mammogram image processing parameter settings vers",   Electromedica   ,Vol.71   ,Issue 1   ,2003
    View Artical

  34. Rafael C.Gonzalez and Richard E.Woods,   "Digital Image Processing",   2nd Edition. Addison-Wesley   ,1993
    View Artical

  35. Rafayah, M., Qutaishat, M., and Abdallah, M.,   "Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural",   Expert. Syst. Appl.   ,Vol.28   ,Issue 4   ,2005
    View Artical

  36. Rakowski, W., and Clark, M. A.,   "Do groups of women aged 50? 75 match the national average mammography rate?",   Am. J. Prev.Med   ,Vol.15   ,Issue 3   ,1998
    View Artical

  37. Rijnsburger, A. J., van Oortmarssen, G. J., Boer, R., Draisma, G.,Miler,A. B., et al,   "Mammography benefit in the Canadian NationalBreast Screening Study-2: a model evaluation",   Int. J. Cancer   ,Vol.110   ,Issue 5   ,2004
    View Artical

  38. S. Lai, X.Li, and W. Bischof,   "On techniques for detecting circumscribed masses in mammograms",   IEEE Trans. Medical Imaging   ,Vol.8   ,Issue 4   ,1989
    View Artical

  39. S.sahebbasha, 2dr.k.satya prasad,   "automatic detection of breast cancer mass in mammograms using morphological operators and fuzzy c ?m   ,2008
    View Artical

  40. Sameti, M., and Ward, R. K.,   "A fuzzy segmentation algorithm for mammogram partitioning",   In: Doi, K., Giger, M. L., Nishikawa, R. M., and S   ,1996
    View Artical

  41. T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R.Silverman, & A.Y.Wu,   "An efficient k-means clustering algorithm Analysis and implementation",   Proc. IEEE Conf. Computer Vision and Pattern Recog   ,2002
    View Artical

  42. T.Wang and N.Karayaiannis,   "Detection of microcalcification in digital mammograms using wavelets",   IEEE Trans. Medical Imaging   ,Vol.17   ,Issue 4   ,1998
    View Artical

  43. Verma, B., and Panchal, R.,   "Neural networks for the classification of benign and malignant patterns in digital mammograms",   In:Fulcher, J.(Ed.), Advances in applied artificia   ,2006
    View Artical

  44. Verma, B., and Zakos, J. A,   "Computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction ",   IEEE T. Inf. Technol. Biomed   ,Vol.5   ,Issue 1   ,2001
    View Artical

  45. W Siedlecki and J.Skalansky,   "on automatic feature selection",   Int. J.Pattern Recog. Art.   ,Vol.2   ,Issue 2   ,1998
    View Artical