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Computational Intelligence Applications in Modeling and Control pp 397–430Cite as

Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 575))

Abstract

Breast cancer is the common form of cancer and leading cause of mortality among woman, especially in developed countries. In western countries about 53–92 % of the population has this disease. As with any form of cancer, early detection and diagnosis of breast cancer can increase the survival rate. Mammography is the current diagnostic method for early detection of breast cancer. Breast parenchymal patterns are not stable between patients, between left and right breasts, and even within the same breast from year to year in the same patient. Breast cancer has a varied appearance on mammograms, from the obvious spiculated masses, to very subtle asymmetries noted on only one view, to faint calcifications seen only with full digital resolution or a magnifying glass. The large volume of cases requiring interpretation in many practices is also daunting, given the number of women in the population for whom yearly screening mammography is recommended. It seems obvious that this difficult task could likely be made less error prone with the help of computer algorithms. Computer-aided detection (CAD) systems have been shown to be capable of reducing false-negative rates in the detection of breast cancer by highlighting suspicious masses and microcalcifications on mammograms. These systems aid the radiologist as a ‘second opinion’ in detecting cancers and the final decision is taken by the radiologist. A supervised machine learning algorithm is investigated—Differential Evolution Optimized Wavelet Neural Network (DEOWNN) for detection of abnormalities in mammograms. Differential Evolution (DE) is a population based optimization algorithm based on the principle of natural evolution, which optimizes real parameters and real valued functions. By utilizing the DE algorithm, the parameters of the Wavelet Neural Network (WNN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture based features of the abnormal breast tissues prior to classification. Then differential evolution optimized wavelet neural network classifier is applied at the end to determine whether the given input data is normal or abnormal. The performance of the computerized decision support system is evaluated using a mini database from Mammographic Image Analysis Society (MIAS) and images collected from mammogram screening centres.

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References

  1. Freer, T.W., Ulissey, M.J.: Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology 220, 781–786 (2001)

    Article  Google Scholar 

  2. Tsui, P.-H., Liao, Y.-Y., Chang, C.-C., Kuo, W.-H., Chang, K.-J., Yeh, C.-K.: Classification of benign and malignant breast tumors by 2-d analysis based on contour description and scatterer characterization. IEEE Trans. Med. Imaging 29(2), 513–522 (2010)

    Article  Google Scholar 

  3. Giger, M.L., Karssemeijer, N., Armato, S.G.: Computer aided diagnosis in medical imaging. IEEE Trans. Med. Imaging 20, 1205–1208 (2001)

    Article  Google Scholar 

  4. Sheppard, L.M.: Not your mother’s mammography. IEEE Spectr 39, 56–57 (2002)

    Article  Google Scholar 

  5. Zheng, B., Chang, Y.H., Gur, D.: Computerized Detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis. Acad. Radiol. 2, 959–966 (1995)

    Google Scholar 

  6. Baker, J.A., Rosen, E.L., Lo, J.Y., Gimenez, E.I., Walsh, R., Soo, M.S.: Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. Am. J. Roentgenol. 81, 1083–1088 (2003)

    Article  Google Scholar 

  7. Tourassi, G.D., Floyd Jr, C.E.: Performance evaluation of an information-theoretic CAD scheme for the detection of mammographic architectural distortion. In: Proceedings of SPIE—The International Society for Optical Engineering, vol. 5370, pp. 59–66 (2004)

    Google Scholar 

  8. Lau, T.K., Bischof, W.F.: Automated detection of breast tumors using the asymmetry approach. Comput. Biomed. Res. 24, 273–295 (1991)

    Article  Google Scholar 

  9. Yin, F.F., Giger, M.L., Doi, K., Vyborny, C.J., Schmidt, R.A.: Computerized detection of masses in digital mammograms: automated alignment of breast images and its effect on bilateral-subtraction technique. Med. Phys. 21, 445–452 (1994)

    Article  Google Scholar 

  10. Chan, H.P., Doi, K., Vyborny, C.J., Schmidt, R.A., Metz, C.E., Lam, K.L., Ogura, T., Wu, Y., MacMahon, H.: Improvement in radiologists detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. Invest. Radiol. 25, 102–1110 (1990)

    Article  Google Scholar 

  11. Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn. 36, 2967–2991 (2003)

    Article  MATH  Google Scholar 

  12. Motakis, E., Ivshina, A.V., Kuznetsov, V.A.: Data-driven approach to predict survival of cancer patients. IEEE Eng. Med. Biol. Mag. 28, 58–66 (2009)

    Article  Google Scholar 

  13. Bird, R.E., Wallace, T.W., Yankaskas, B.C.: Analysis of cancers missed at screening mammography. Radiology 184, 613–617 (1992)

    Article  Google Scholar 

  14. Azar, A.T., El-Metwally, S.M.: Decision tree classifiers for automated medical diagnosis. Neural Comput. Appl. 23(7–8), 2387–2403 (2013). doi:10.1007/s00521-012-1196-7

    Article  Google Scholar 

  15. Azar, A.T.: Statistical analysis for radiologists’ interpretations variability in mammograms. Int. J. Syst. Biol. Biomed. Technol. (IJSBBT) 1(4), 28–46 (2012)

    Google Scholar 

  16. Azar, A.T., El-Said, S.A.: Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput. Appl. 24(5), 1163–1177 (2014). doi:10.1007/s00521-012-1324-4

    Article  Google Scholar 

  17. Moftah, H.M., Azar, A.T., Al-Shammari, E.T., Ghali, N.I., Hassanien, A.E., Shoman, M.: Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput. Appl. 24(7–8), 1917–1928 (2014). doi:10.1007/s00521-013-1437-4

    Article  Google Scholar 

  18. Tiedeu, A., Daul, C., Kentsop, A., Graebling, P., Wolf, D.: Texture-based analysis of clustered microcalcifications detected on mammograms. Digit. Signal Proc. 22, 124–132 (2012)

    Article  MathSciNet  Google Scholar 

  19. Yu, S.-N., Huang, Y.-K.: Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features. Expert Syst. Appl. 37(7), 5461–5469 (2010)

    Article  MathSciNet  Google Scholar 

  20. Anna, N., Ioannis, S., Spyros, G., Filippos, N., Nikolaos, S., Eleni, A., George, S., Lena, I.: Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans. Inf Technol. Biomed. 12, 731–738 (2008)

    Article  Google Scholar 

  21. Azar, A.T., El-Said, S.A.: Probabilistic neural network for breast cancer classification. Neural Comput. Appl. 23(6), 1737–1751 (2013). doi:10.1007/s00521-012-1134-8

    Article  Google Scholar 

  22. Mudigonda, N.R., Rangayyan, R.M., Leo Desautels, J.E.: Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imaging 20, 1215–1227 (2001)

    Article  Google Scholar 

  23. Li, H., Wang, Y., Liu, K.J.R., Lo, S.C.B., Matthew, T.: Computerized radiographic mass detection part I: lesion site selection by morphological enhancement and contextual segmentation. IEEE Trans. Med. Imaging 20, 289–301 (2001)

    Article  Google Scholar 

  24. Gao, X., Wang, Y., Li, X., Tao, D.: On combining morphological component analysis and concentric morphology model for mammographic mass detection. IEEE Trans. Inf Technol. Biomed. 14, 266–273 (2010)

    Article  Google Scholar 

  25. Mudigonda, N.R., Rangayyan, R.M., Leo Desautels, J.E.: Gradient and texture analysis for the classification of mammographic masses. IEEE Trans. Med. Imaging 19(10), 1032–1043 (2000)

    Google Scholar 

  26. Suliga, M., Deklerck, R., Nyssen, E.: Markov random field-based clustering applied to the segmentation of masses in digital mammograms. Comput. Med. Imaging Graph. 32, 502–512 (2008)

    Article  Google Scholar 

  27. Grim, J., Somol, P., Haindl, M., Danes, J.: Computer aided evaluation of screening mammograms based on local texture model. IEEE Trans. Image Process. 18, 765–773 (2009)

    Article  MathSciNet  Google Scholar 

  28. Heine, J.J., Deans, S.R., Cullers, D.K., Stauduhar, R., Laurence, P.: Multiresolution statistical analysis of high-resolution digital mammograms. IEEE Trans. Med. Imaging 16, 503–515 (1997)

    Article  Google Scholar 

  29. Kupinski, M.A., Giger, M.L.: Automated seeded lesion segmentation on digital mammograms. IEEE Trans. Med. Imaging 17(4), 510–517 (1998)

    Google Scholar 

  30. Nakayama, R., Uchiyama, Y., Yamamoto, K., Watanabe, R., Namba, K. Computer aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms. IEEE Trans. Biomed. Eng. 53(2), 273–283 (2006)

    Google Scholar 

  31. Cheng, H.-D., Lui, Y.M., Freimanis, R.I.: A novel approach to microcalcification detection using fuzzy logic technique. IEEE Trans. Med. Imaging 17, 442–450 (1998)

    Article  Google Scholar 

  32. Wang, T.C., Karayiannis, N.B.: Detection of microcalcifications in digital mammograms using wavelets. IEEE Trans. Med. Imaging 17, 498–509 (1998)

    Article  Google Scholar 

  33. Eltoukhy, M.M., Faye, I., Samir, B.B.: Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Comput. Med. Imaging Graph. 34, 269–276 (2010)

    Article  Google Scholar 

  34. Verma, B., McLeod, P., Klevansky, A.: Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Expert Syst. Appl. 37, 3344–3351 (2010)

    Article  Google Scholar 

  35. Teo, J., Chen, Y., Soh, C.B., Gunawan, E., Low, K.S., Putti, T.C., Wang, S.-C.: Breast lesion classification using ultra wideband early time breast lesion response. IEEE Trans. Antennas Propag. 58, 2604–2613 (2010)

    Article  Google Scholar 

  36. Peng, R., Hao, C., Varshney, P.K.: Noise-enhanced detection of micro-calcifications in digital mammograms. IEEE J. Sel. Top. Sign. Process. 3, 62–73 (2009)

    Article  Google Scholar 

  37. Suckling, J., Parker, J.: The Mammographic Images Analysis Society Digital Mammogram Database. In: Proceedings of 2nd International Workshop on Digital Mammography, UK, pp. 375–378 (1994)

    Google Scholar 

  38. Anderson, E.D., Muir, B.B., Walsh, J.S., Kirkpatrick, A.E.: The efficacy of double reading mammograms in breast screening. Clin. Radiol. 49, 248–251 (1994)

    Article  Google Scholar 

  39. Thurfjell, E.L., Lernevall, K.A., Taube, A.A.: Benefit of Independent Double Reading in a Population based Mammography Screening Program. Radiology 191, 241–244 (1994)

    Article  Google Scholar 

  40. Gonzales, R.C., Woods, R.E.: Digital image processing. Prentice Hall, Upper Saddle River, NJ (2002)

    Google Scholar 

  41. Rafael, C., Gonzalez, R.E., Woods S.L.: Digital Image Processing Using MATLAB. Pearson Education India (2005)

    Google Scholar 

  42. Tsai, D.-Y., Kojima, K.: Measurement of texture features of medical images and its application to computer aided diagnosis in cardiomyopathy. Measurement 37, 284–292 (2005)

    Article  Google Scholar 

  43. Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based feature distributions. Pattern Recogn. 29, 51–59 (1996)

    Article  Google Scholar 

  44. Sutton, R.N., Hall, E.L.: Texture measures for automatic classification of pulmonary disease. IEEE Trans. Comput. C-21, 667–676 (1972)

    Google Scholar 

  45. Harms, H., Gunzer, U., Aus, H.M.: Combined local color and texture analysis of stained cells. Comput. Vis. Graphics Image Process. 33, 364–376 (1986)

    Article  Google Scholar 

  46. Insana, M.F., Wagner, R.F., Garra, B.S., Brown, D.G., Shawker, T.H.: Analysis of ultrasound image texture via generalized Rician statistics. Opt. Eng. 25, 743–748 (1986)

    Article  Google Scholar 

  47. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979)

    Article  Google Scholar 

  48. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973)

    Google Scholar 

  49. Laws, K.J.: Texture energy measures. Proceeding DARPA Image Understanding Workshop, pp. 47–51 (1979)

    Google Scholar 

  50. Christodoulou, C.I., Pattichis, C.S., Pantziaris, M., Nicolaides, A.: Texture-based classification of atherosclerotic carotid plaques. IEEE Trans. Med. Imaging 22, 902–912 (2003)

    Article  Google Scholar 

  51. Chauhan, N., Ravi, V., Karthik Chandra, D.: Differential evolution trained wavelet neural network application to bankruptcy prediction in banks. Expert Syst. Appl. 36, 7659–7665 (2009)

    Article  Google Scholar 

  52. McCulloch, W.S., Pitts, W.: A logical study of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    Article  MATH  MathSciNet  Google Scholar 

  53. Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, New York (1995)

    Google Scholar 

  54. Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  55. Zhang, J., Walter, G.G., Miao, Y., Lee, W.N.W.: Wavelet neural networks for function learning. IEEE Trans. Signal Process. 43, 1485–1497 (1995)

    Article  Google Scholar 

  56. Zhang, Q., Benvniste, A.: Wavelet networks. IEEE Trans. Neural Netw. 3, 889–898 (1992)

    Article  Google Scholar 

  57. Daubechies, I.: Time-frequency localization operators: a geometric phase space approach. IEEE Trans. Inf. Theory 34, 605–612 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  58. Zheng, B., Qian, W., Clarke, L.P.: Digital mammography mixed feature neural network with spectral entropy decision for detection of microcalcifications. IEEE Trans. Med. Imaging 15, 589–597 (1996)

    Article  Google Scholar 

  59. Metz, C.E.: ROC methodology in radiologic imaging, Invest Radiol 21(9), 720–733 (1986)

    Google Scholar 

  60. Zweig, M.H., Campbell, G.: Receiver operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561–577 (1993)

    Google Scholar 

  61. Youden, W.J.: An index for rating diagnostic tests. Cancer 3, 32–35 (1950)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Noorul Islam University, Kumaracoil, Tamil Nadu, India

    J. Dheeba

  2. BSNL, Nagercoil, Tamil Nadu, India

    N. Albert Singh

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  1. J. Dheeba
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  2. N. Albert Singh
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Correspondence to J. Dheeba .

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Editors and Affiliations

  1. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  2. Research and Development Centre, Vel Tech University, Chennai, India

    Sundarapandian Vaidyanathan

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Dheeba, J., Albert Singh, N. (2015). Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study. In: Azar, A., Vaidyanathan, S. (eds) Computational Intelligence Applications in Modeling and Control. Studies in Computational Intelligence, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-11017-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-11017-2_16

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