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Applications of Intelligent Optimization in Biology and Medicine pp 159–180Cite as

PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 96))

Abstract

Early prediction of breast density is clinically significant as there is an association between the risk of breast cancer development and breast density. In the present work, the performance of two computer aided diagnostic (CAD) systems has been compared for classification of breast tissue density. The work has been carried out on MIAS dataset with 322 mammographic images consisting of 106 fatty and 216 dense images. The ROIs have been selected from densest region (i.e., the center of each image, ignoring the pectoral muscle) of each mammogram. The total dataset consisted of 322 ROIs (106 fatty ROIs and 216 dense ROIs). Five statistical texture features namely, mean, standard deviation, entropy, kurtosis and skewness are evaluated from Laws’ texture energy images resulting from Laws’ masks of length 5, 7 and 9. The texture feature vectors computed from Laws’ masks of different lengths are then subjected to principal component analysis (PCA) for reduction in feature space dimensionality. The SVM and PNN classifiers are used for the classification task. It is observed that the highest classification accuracy of 92.5 % is achieved with first four principal components derived from texture features computed with Laws’ masks of length 7 by using PNN classifier and the highest classification accuracy of 94.4 % is achieved with first four principal components derived from texture features computed with Laws’ masks of length 5 by using SVM classifier. It can be concluded that the first four principal components derived from Laws’ texture energy images resulting from Laws’ masks of length 5 are sufficient to account for textural changes exhibited by fatty and dense mammograms. The promising results obtained by the proposed CAD design indicate its usefulness to assist radiologists for breast density classification.

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References

  1. Breast Cancer Awareness Month in October. World Health Organization (2012)

    Google Scholar 

  2. Globocan 2012: Estimated cancer incidence, mortality and prevalence worldwide. IARC (2012)

    Google Scholar 

  3. Breast cancer early detection: The importance of finding breast cancer early. American Cancer Society (2013)

    Google Scholar 

  4. Jagannath, H.S., Virmani, J., Kumar, V.: Morphological enhancement of microcalcifications in digital mammograms. J. Inst. Eng. B (India) 93(3), 163–172 (2012)

    Google Scholar 

  5. Virmani, J., Kumar, V.: Quantitative evaluation of image enhancement techniques. In: Proceedings of International Conference on Biomedical Engineering and Assistive Technology (BEATS-2010), NIT Jalandhar, India (2010)

    Google Scholar 

  6. Wolfe, J.N.: Breast patterns as an index of risk for developing breast cancer. Am. J. Roentgenol. 26, 1130–1139 (1976)

    Article  Google Scholar 

  7. Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37, 2486–2492 (1976)

    Article  Google Scholar 

  8. Boyd, N.F., Martin, L.J., Yaffe, M.J., Minkin, S.: Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res. 13, 223–234 (2011)

    Article  Google Scholar 

  9. Boyd, N.F., Martin, L.J.: Potential mechanism of breast cancer risk associated with mammographic density: hypothesis based on epidemiological evidence. Breast Cancer Res. 10, 201–214 (2008)

    Article  Google Scholar 

  10. Mustra, M., Grgic, M., Delac, K.: Breast density classification using multiple feature selection. AUOTMATIKA J. Control Meas. Electron. Comput. Commun. 53(4), 362–372 (2012)

    Google Scholar 

  11. Karssemeijer, N.: Automated classification of parenchymal patterns in mammograms. Phys. Med. Biol. 43, 365–378 (1998)

    Google Scholar 

  12. Bovis, K., Singh, S.: Classification of mammographic breast density using a combined classifier paradigm. In: Conference on Medical Image Understanding and Analysis, pp. 1–4 (2002)

    Google Scholar 

  13. Petroudi, S., Kadir, T., Brady, M.: Automatic classification of mammographic parenchymal patterns: a statistical approach. In: Proceedings of 25th IEEE International Conference on Engineering in Medicine and Biology Society, vol. 1, pp. 798–801. Cancun, Mexico (2003)

    Google Scholar 

  14. Oliver, A., Freixenet, R., Zwiggelaar, R.: Automatic classification of breast density. In: Proceedings of 12th IEEE International Conference on Image Processing, vol. 2, pp. 1258–1261 (2005)

    Google Scholar 

  15. Bosch, A., Munoz, X., Oliver, A., Marti, J.: Modeling and Classifying Breast Tissue Density in Mammograms. Comput. Vision Pattern Recognit. IEEE Comput. Soc. Conf. 2, 1552–1558 (2006)

    Google Scholar 

  16. Muhimmah, I., Zwiggelaar, R.: Mammographic density classification using multiresolution histogram information. In: Proceedings of International Special Topic Conference on Information Technology in Biomedicine. Ioannina, Greece (2006)

    Google Scholar 

  17. Castella, C., Kinkel, K., Sottas, P.E., Verdun, F.R., Bochud, F.O.: Semiautomatic mammographic parenchymal pattern classification using multiple statistical features. Acad. Radiol. 14, 1486–1499 (2007)

    Article  Google Scholar 

  18. Oliver, A., Freixenet, J., Marti, R., Pont, J., Pérez, E., Denton, E.R., Zwiggelaar, R.: A novel breast tissue density classification methodology. IEEE Trans. Inf. Technol. Biomed. 12(1), 55–65 (2008)

    Article  Google Scholar 

  19. Subashini, T.S., Ramalingam, V., Palanivel, S.: Automated assessment of breast tissue density in digital mammograms. Comput. Vis. Image Underst. 114, 33–43 (2010)

    Article  Google Scholar 

  20. Tzikopoulos, S.D., Mavroforakis, M.E., Georgiou, H.V., Dimitropoulos, N., Theodoridis, S.: A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Comput. Methods Programs Biomed. 102, 47–63 (2011)

    Article  Google Scholar 

  21. Miller, P., Astley, S.: Classification of breast tissue by texture analysis. Image Vis. Comput. 10, 277–282 (1992)

    Article  Google Scholar 

  22. Wang, X.H., Good, W.F., Chapman, B.E., Chang, Y.H., Poller, W.R., Chang, T.S., Hardesty, L.A.: Automated assessment of the composition of breast tissue revealed on tissue thickness corrected mammography. Am. J. Roentgenol. 180, 257–262 (2003)

    Article  Google Scholar 

  23. Li, J.B.: Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for breast cancer diagnosis. J. Med. Syst. 36(4), 2235–2244 (2012)

    Article  Google Scholar 

  24. Sharma, V., Singh, S.: CFS-SMO based classification of breast density using multiple texture models. Med. Biol. Eng. Comput. 52(6), 521–529 (2014). Springer

    Article  MathSciNet  Google Scholar 

  25. Colin, C., Prince, V., Valette, P.J.: Can mammographic assessments lead to consider density as a risk factor for breast cancer? Eur. J. Radiol. 82, 404–411 (2013)

    Google Scholar 

  26. Warren, R.: Hormones and mammographic breast density. Maturitas Eur. Menopause J. 49, 67–78 (2004)

    Google Scholar 

  27. Ciatto, S., Houssami, N., Apruzzese, A., Bassetti, E., Brancato, B., Carozzi, F., Catarzi, S., Lamberini, M.P., Marcelli, G., Pellizzoni, R., Pesce, B., Risso, G., Russo, F., Scorsolini, A.: Categorizing breast Mammographic density: intra- and inter-observer reproducibility of BIRADS density category. Breast 14(4), 269–275 (2004)

    Article  Google Scholar 

  28. Tagliafico, A., Tagliafico, G., Tosto, S., Chiesa, F., Martinoli, C., Derchi, L.E., Calabrese, M.: Mammographic density estimation: comparison among BI-RADS categories, a semi-automated software and a fully automated one. Breast 18(1), 35–40 (2009)

    Article  Google Scholar 

  29. Boyd, N.F., Rommens, J.M., Vogt, K., Lee, V., Hopper, J.L., Yaffe, M.J., Paterson, A.D.: Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol. 6, 798–808 (2005)

    Article  Google Scholar 

  30. Cheddad, A., Czene, K., Eriksson, M., Li, J., Easton, D., Hall, P., Humphreys, K.: Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer. PLoSOne 9(10), e110690 (2014)

    Google Scholar 

  31. Eng, A., Gallant, Z., Shepherd, J., McCormack, V., Li, J., Dowsett, M., Vinnicombe, S., Allen, S., Silva, I.: Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods. Breast Cancer Res. 16, 439–450 (2014)

    Article  Google Scholar 

  32. Keller, B.M., Nathan, D.L., Wang, Y., James, Y., Gee, J.C., Conant, E.F., Kontos, D.: Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation. Med. Phys. 39(8), 4903–4917 (2012)

    Article  Google Scholar 

  33. AlMousa, D.S., Brennan, P.C., Ryan, E.A., Lee, W.B., Tan, J., Mello-Thoms, C.: How mammographic breast density affects radiologists’ visual search patterns. Acad. Radiol. 21(11), 1386–1393 (2014)

    Article  Google Scholar 

  34. Laws, K.I.: Rapid texture identification. Proc. SPIE Image Process. Missile Guidance 238, 376–380 (1980)

    Article  Google Scholar 

  35. Rachidi, M., Marchadier, A., Gadois, C., Lespessailles, E., Chappard, C., Benhamou, C.L.: Laws’ masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis. Skeletal Radiol. 37, 541–548 (2008)

    Article  Google Scholar 

  36. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: Characterization of primary and secondary malignant liver lesions from B-mode ultrasound. J. Digital Imaging 26(6), 1058–1070 (2013)

    Article  Google Scholar 

  37. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: PCA-SVM based CAD system for focal liver lesions using B-mode ultrasound images. Defence Sci. J. 63(5), 478–486 (2013)

    Article  Google Scholar 

  38. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: Prediction of cirrhosis from liver ultrasound B-mode images based on Laws’ mask analysis. In: Proceedings of the IEEE International Conference on Image Information Processing, ICIIP-2011, pp. 1–5. Himachal Pradesh, India (2011)

    Google Scholar 

  39. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: Neural network based ensemble based CAD system for focal liver lesions from B-mode ultrasound. J. Digital Imaging 27(4), 520–537 (2014)

    Article  MATH  Google Scholar 

  40. Seng, G.H., Chai, H.Y., Swee, T.T.: Research on Laws’ mask texture analysis system reliability. Res. J. Appl. Sci. Eng. Technol. 7(19), 4002–4007 (2014)

    Google Scholar 

  41. Suckling, J.: The mammographic image analysis society digital mammogram database. In: Proceedings of International Workshop Digital Mammography, vol. 1069, pp. 375–378 (1994)

    Google Scholar 

  42. Li, H., Giger, M.L., Huo, Z., Olopade, O.I., Lan, L., Weber, B.L.,Bonta, I.W.: Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. Med. Phys. 31(3), 549–555 (2005)

    Google Scholar 

  43. Hassanein, A.E., Kim, T.H.: Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks. J. Appl. Logic 10(4), 274–284 (2012)

    Google Scholar 

  44. Anter, A.M., ElSoud, M.A., Hassanien, A.E.: Automatic mammographic parenchyma classification according to BIRADS dictionary. Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies. IGI Global. pp. 22–37 (2014)

    Google Scholar 

  45. Lodwick, G.S., Haun, C.L., Smith, W.E., Keller, R.F., Robertson, E.D.: Computer diagnosis of primary bone tumor. Radiology 80(2), 273–275 (1963)

    Article  Google Scholar 

  46. Winsbarg, F., Elkin, M., Macy, J., Bordaz, V., Weymouth, W.: Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology 89(2), 211–215 (1967)

    Article  Google Scholar 

  47. Chan, H.P., Doi, K., Vybrony, C.J., Schmidt, R.A., Metz, C., Lam, K.L., Ogura, T., Wu, Y., MacMahon, H.: Improvement in radiologists’ detection of clustered micro-calcifications on mammograms: the potential of computer-aided diagnosis. Instigative Radiol. 25(10), 1102–1110 (1990)

    Article  Google Scholar 

  48. Giger, M.L., Doi, K., MacMahon, H., Nishikawa, R.M., Hoffman, K.R., Vyborny, C.J., Schmidt, R.A., Jia, H., Abe, K., Chen, X.: An intelligent workstation for computer-aided diagnosis. Radiographics 13(3), 647–656 (1993)

    Article  Google Scholar 

  49. Doi, K., MacMahon, H., Katsuragawa, S., Nishikawa, R.M., Jiang, Y.: Computer-aided diagnosis in radiology: potential and pitfalls. Eur. J. Radiol. 31(2), 97–109 (1997)

    Article  MATH  Google Scholar 

  50. Tourassi, G.D.: Journey toward computer-aided diagnosis: role of image texture analysis. Radiology 213(2), 317–320 (1999)

    Article  Google Scholar 

  51. Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4-5), 198–211 (2007)

    Google Scholar 

  52. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: A rapid approach for prediction of liver cirrhosis based on first order statistics. In: Proceedings of the IEEE International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT-2011, pp. 212–215 (2011)

    Google Scholar 

  53. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: Prediction of cirrhosis based on singular value decomposition of gray level co-occurrence matrix and a neural network classifier. In: Proceedings of the IEEE International Conference on Development in E-Systems Engineering, DeSe-2011, pp. 146–151 (2011)

    Google Scholar 

  54. Amendolia, S.R., Cossu, G., Ganadu, M.L., Galois, B., Masala, G.L., Mura, G.M.: A comparative study of K-nearest neighbor, support vector machine and multi-layer perceptron for thalassemia screening. Chemom. Intell. Lab. Syst. 69, 13–20 (2003)

    Article  Google Scholar 

  55. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound. J. Med. Eng. Technol. 37(4), 292–306 (2013)

    Article  Google Scholar 

  56. Specht, D.F.: Probabilistic neural networks. Neural Netw. 1(3), 109–118 (1990)

    Article  Google Scholar 

  57. Specht, D.F., Romsdahl, H.: Experience with adaptive probabilistic neural network and adaptive general regression neural network. Proc. IEEE Int. Conf. Neural Netw. 2, 1203–1208 (1994)

    Google Scholar 

  58. Georgiou, V.I., Pavlidis, N.G., Parsopoulos, K.E., Vrahatis, M.N.: Optimizing the performance of probabilistic neural networks in a bioinformatics task. In: Proceedings of the EUNITE 2004 Conference, pp. 34–40 (2004)

    Google Scholar 

  59. Chang, C.C., Lin, C.J.: LIBSVM, A library of support vector machines (2012)

    Google Scholar 

  60. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: SVM based characterization of liver cirrhosis by singular value decomposition of GLCM matrix. Int. J. Artif. Intell. Soft Comput. 3(3), 276–296 (2013)

    Article  Google Scholar 

  61. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. J. Digital Imaging 26(3), 530–543 (2012)

    Article  Google Scholar 

  62. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound. Int. J. Convergence Comput. 1(1), 19–37 (2013)

    Article  Google Scholar 

  63. Hassanien, A.E., Bendary, N.E., Kudelka, M., Snasel, V.: Breast cancer detection and classification using support vector machines and pulse coupled neural network. In: The Proceedings of 3rd international Conference on Intelligent Human Computer Interaction IHCI 2011, pp. 269–279. Czech Republic, Prague (2013)

    Google Scholar 

  64. Hariri, E.E., Bendary, N.E., Hassanien, A.E., Badr, A.: Automated ripeness assessment system of tomatoes using PCA and SVM techniques. Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies. IGI Global, pp. 101–130 (2014)

    Google Scholar 

  65. Azar, A.T., El-Said, S.A.: Performance analysis of support vector machine classifiers in breast cancer mammography recognition. Neural Comput. Appl. 24(5), 1163–1177 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Kriti & Jitendra Virmani

  2. Bengal College of Engineering and Technology, Durgapur, India

    Nilanjan Dey

  3. Indian Institute of Technology, Roorkee, India

    Vinod Kumar

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  1. Kriti
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  2. Jitendra Virmani
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  3. Nilanjan Dey
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  4. Vinod Kumar
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Correspondence to Jitendra Virmani .

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Editors and Affiliations

  1. Information Technology Department, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Department of Information Systems and Computing, Brunel University, London, United Kingdom

    Crina Grosan

  3. Faculty of Computers and information, Ain Shams University, Cairo, Egypt

    Mohamed Fahmy Tolba

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Kriti, Virmani, J., Dey, N., Kumar, V. (2016). PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification. In: Hassanien, AE., Grosan, C., Fahmy Tolba, M. (eds) Applications of Intelligent Optimization in Biology and Medicine. Intelligent Systems Reference Library, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-319-21212-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-21212-8_7

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