Search WWW Search inass.org
»Journal Description
»Topics
»Call for Papers and Reviewers
»Author Guidelines
»Contents & Papers
»Call for Special Issues
»SCOPUS
 
»IEEE CIS
»INNS
»IEEE IS
DOI: http://dx.doi.org/10.22266/ijies2017.0228.08

Automatic Detection and Classification of Masses in Digital Mammograms

Author(s):

Shankar Thawkar1*,Ranjana Ingolikar2


Affiliations:

1Department of Information Technology, Hindustan College of Science and Technology, Mathura, India
2Department of Computer Science, S.F.S. College, Nagpur, India







Abstract:

Breast Cancer is still one of the leading cancers in women. Mammography is the best tool for early detection of breast cancer. In this work methods for automatic detection and classification of masses into benign or malignant has been proposed. The suspicious masses are detected automatically by performing image segmentation with Otsu’s global thresholding technique, morphological operations and watershed transformation. Twenty-five features based on intensity, texture and shape are extracted from each of the 651 mammograms obtained from Database of Digitized Screen-film Mammograms. The Eight most significant features selected by step-wise Linear Discriminate Analysis are used to classify masses using Fisher’s Linear Discriminate Analysis, Support Vector Machine and Multilayer Perceptron with two training algorithms Levenberg-Marquardt and Bayesian Regularization. The performance evaluation of classifiers indicates that MLP is better than both LDA and SVM. MLP-RBF has 98.9% accuracy with area under Receiver Operating Characteristics curve AZ=0.98±0.007, MLP-LM 96.0% accuracy with AZ=0.97±0.007, SVM 91.4% accuracy with AZ=0.956±0.009 and LDA 90.3% accuracy with AZ=0.956±0.009. All the results achieved are promising when compared with some existing work.


Keywords:

Digital mammograms, Neural network, Linear discriminant analysis, Feature selection, Support vector machine, Receiver operating characteristics curve.


Full Text:




References:
  1. N. C. I. (NCI),” Cancer stat fact sheets: Cancer of the breast”, Available at: http:// www.seer.cancer.gov/statfacts/html /breast.html, May 2009.
  2. B. Acha, C. Serrano, R.M. Rangayyan, and J.L. Desautels, “Detection of microcalcifications in mammograms”, Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer. SPIE, Bellingham, 2006.
  3. H.D. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou, “Computer-aided detection and classification of microcalcifications in mammograms: a survey”, Pattern recognition, 36(12), pp.2967-2991, 2003.
  4. I. Christoyianni, E. Dermatas, and G. Kokkinakis, “Fast detection of masses in computer-aided mammography”, Signal Processing Magazine, IEEE, 17(1), pp.54-64, 2000.
  5. P. Skaane, K. Engedal, and A. Skjennald, “Interobserver variation in the interpretation of breast imaging: comparison of mammography, ultrasonography, and both combined in the interpretation of palpable non calcified breast masses”, Acta Radiologica, 38(4), pp.497-502, 1997.
  6. F. Moayedi, Z. Azimifar, R. Boostani, and S. Katebi, “Contourlet-based mammography mass classification”, In Image Analysis and Recognition ,pp. 923-934, Springer Berlin Heidelberg, 2007.
  7. L. Arbach, J.M. Reinhardt, D.L. Bennett, and G. Fallouh, “Mammographic masses classification: comparison between backpropagation neural network (BNN), K nearest neighbours (KNN), and human readers”, In Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on, vol. 3, pp. 1441-1444. IEEE, 2003.
  8. A.W. Whitney, “A direct method of nonparametric measurement selection”, IEEE Transactions on Computers, 100(9), pp.1100-1103, 1971.
  9. I. Christoyianni, E. Dermatas, and G. Kokkinakis, “Neural classification of abnormal tissue in digital mammography using statistical features of the texture”, In Electronics, Circuits and Systems, 1999. Proceedings of ICECS'99. The 6th IEEE International Conference on, vol. 1, pp. 117-120. IEEE, 1999.
  10. W.P. Kegelmeyer Jr, J.M. Pruneda, P.D. Bourland, A. Hillis, M.W. Riggs, and M.L. Nipper, “Computer-aided mammographic screening for spiculated lesions”, Radiology, 191(2), pp.331-337, 1994.
  11. R.M. Rangayyan, N.R. Mudigonda, and J.L. Desautels, “Boundary modelling and shape analysis methods for classification of mammographic masses,” Medical and Biological Engineering and Computing, 38(5), pp.487-496, 2000.
  12. H.P. Chan, D. Wei, M.A. Helvie, B. Sahiner, D.D. Adler, M.M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space”, Physics in medicine and biology, 40(5), p.857, 1995.
  13. L. de Oliveira Martins, G.B. Junior, E.C. da Silva, A.C. Silva, and A.C. de Paiva, “Classification of breast tissues in mammogram images using Ripley’s K function and support vector machine”, In Image Analysis and Recognition, pp. 899-910. Springer Berlin Heidelberg, 2007.
  14. A. Petrosian, H.P. Chan, M.A. Helvie, M.M. Goodsitt, and D.D. Adler, “Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis”, Physics in Medicine and Biology, 39(12), p.2273, 1994.
  15. R. Frigato and E. Silva, “Mathematical morphology application to features extraction in digital images”, ASPRS, Pecora, 17, 2008.
  16. Z. Yu-qian, G. Wei-hua, C. Zhen-cheng, T. Jing-tian, and L. Ling-Yun, “Medical images edge detection based on mathematical morphology”, In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference, pp. 6492-6495. IEEE, 2006.
  17. N. Otsu, “Thresholds selection method form grey-level histograms”, IEEE Trans. On Systems, Man and Cybernetics, 9(1), p.1979, 1979.
  18. L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations”, IEEE Transactions on Pattern Analysis & Machine Intelligence, (6), pp.583-598, 1991.
  19. Hu, M.K., “Visual pattern recognition by moment invariants.” information Theory, IRE Transactions on information theory, 8(2), pp.179-187, 1962.
  20. N. Petrick, H.P. Chan, B. Sahiner, and M.A. Helvie, “Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms”, Medical physics, 26(8), pp.1642-1654, 1999.
  21. R.M. Haralick, K. Shanmugam, and I.H. Dinstein, “Textural features for image classification”, IEEE Transactions on Systems, Man and Cybernetics, (6), pp.610-621, 1973.
  22. R.M. Haralick, “Statistical and structural approaches to texture”, Proceedings of the IEEE, 67(5), pp.786-804, 1979.
  23. Y.H. Chou, C.M. Tiu, G.S. Hung, S.C. Wu, T.Y. Chang, and H.K. Chiang, “Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis”, Ultrasound in medicine & biology, 27(11), pp.1493-1498, 2001.
  24. A.V. Alvarenga, W.C.A. Pereira, A.F.C. Infantosi, and C.M. Azevedo, “Morphologic operators applied to breast tumour ultrasound image classification”, In Acoustical imaging (pp. 463-470). Springer Netherlands, 2004.
  25. P. Soille, “Morphological image analysis: principles and applications”, Springer Science & Business Media, 2013.
  26. M. Sameti, R.K. Ward, J. Morgan-Parkes, and B. Palcic, “A method for detection of malignant masses in digitized mammograms using a fuzzy segmentation algorithm”, In Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, vol. 2, pp. 513-516. IEEE, 1997.
  27. H. Li, Y. Wang, K.J. Liu, S.C.B. Lo, and M.T. Freedman, “Computerized radiographic mass detection. II. Decision support by featured database visualization and modular neural networks”, IEEE Transactions on Medical Imaging, 20(4), pp.302-313, 2001.
  28. Z. Huo, M.L. Giger, C.J. Vyborny, F.I. Olopade, and D.E. Wolverton, “Computer-aided diagnosis: Analysis of mammographic parenchymal patterns and classification of masses on digitized mammograms”, In Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, vol. 2, pp. 1017-1020. IEEE, 1998.
  29. B. Sahiner, N. Petrick, H.P. Chan, L.M. Hadjiiski, C. Paramagul, M.A. Helvie, and M.N. Gurcan, “Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization”, IEEE Transactions on Medical Imaging, 20(12), pp.1275-1284, 2001.
  30. M.M. Tatsuoka, and P.R. Lohnes”, Multivariate analysis: Techniques for educational and psychological research”, Macmillan Publishing Co, Inc, 1988.
  31. R.O. Duda, P.E. Hart, and D.G. Stork, “Pattern classification”, John Wiley & Sons, 2012.
  32. P.A. Lachenbruch, “Discriminant Analysis, New York: Hafner”, Lachenbruch Discriminant Analysis 1975, 1975.
  33. K. Bovis, S. Singh, J. Fieldsend, and C. Pinder, “Identification of masses in digital mammograms with MLP and RBF nets”, In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, vol. 1, pp. 342-347. IEEE, 2000.
  34. S. Baeg, and N. Kehtarnavaz, “Texture based classification of mass abnormalities in mammograms”, In Computer-Based Medical Systems, 2000. CBMS 2000. Proceedings. 13th IEEE Symposium on, pp. 163-168. IEEE, 2000.
  35. R.P. Velthuizen, and J.I. Gaviria, “Computerized mammographic lesion description”, In [Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint, vol. 2, pp. 1034-vol. IEEE, 1999.
  36. D.B. Fogel, E.C. Wasson III, E.M. Boughton, and V.W. Porto, “Evolving artificial neural networks for screening features from mammograms”, Artificial Intelligence in Medicine, 14(3), pp.317-326, 1998.
  37. C.E. Floyd, J.Y. Lo, A.J. Yun, D.C. Sullivan, and P.J. Kornguth, “Prediction of breast cancer malignancy using an artificial neural network”, Cancer, 74(11), pp.2944-2948, 1994.
  38. C. Cortes, and V. Vapnik, “Support-vector networks”, Machine learning, 20(3), pp.273-297, 1995.
  39. H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.P. Cai, and H.N. Du, “Approaches for automated detection and classification of masses in mammograms”, Pattern recognition, 39(4), pp.646-668, 2006.
  40. M.T. Wong, X. He, H. Nguyen, and W.C. Yeh, “Mass classification in digitized mammograms using texture features and artificial neural network”, In Neural Information Processing, pp. 151-158. Springer Berlin Heidelberg, 2012
  41. B. Zheng, S.W. Yoon, and S.S. Lam, “Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms”, Expert Systems with Applications, 41(4), pp.1476-1482, 2014.
  42. A.K. Mohanty, M.R. Senapati, and S.K. Lenka, “A novel image mining technique for classification of mammograms using hybrid feature selection”, Neural Computing and Applications, 22(6), pp.1151-1161, 2013.
  43. J.A. Swets, “Measuring the accuracy of diagnostic systems”, Science, 240(4857), pp.1285-1293, 1988.
  44. M. Kubat, R.C. Holte and S. Matwin, “Machine learning for the detection of oil spills in satellite radar images”, Machine learning, 30(2-3), pp.195-215, 1998.
  45. M. Kubat and S. Matwin, “Addressing the curse of imbalanced training sets: one-sided selection,” In ICML, Vol. 97, pp. 179-186, 1997.

INASS Home | Copyright@2008 The Intelligent Networks and Systems Society