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Spatial Enhancement by Dehazing for Detection of Microcalcifications with Convolutional Nets

  • Alessandro Bria
  • Claudio Marrocco
  • Adrian Galdran
  • Aurélio Campilho
  • Agnese Marchesi
  • Jan-Jurre Mordang
  • Nico Karssemeijer
  • Mario Molinara
  • Francesco Tortorella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)

Abstract

Microcalcifications are early indicators of breast cancer that appear on mammograms as small bright regions within the breast tissue. To assist screening radiologists in reading mammograms, supervised learning techniques have been found successful to detect microcalcifications automatically. Among them, Convolutional Neural Networks (CNNs) can automatically learn and extract low-level features that capture contrast and spatial information, and use these features to build robust classifiers. Therefore, spatial enhancement that enhances local contrast based on spatial context is expected to positively influence the learning task of the CNN and, as a result, its classification performance. In this work, we propose a novel spatial enhancement technique for microcalcifications based on the removal of haze, an apparently unrelated phenomenon that causes image degradation due to atmospheric absorption and scattering. We tested the influence of dehazing of digital mammograms on the microcalcification detection performance of two CNNs inspired by the popular AlexNet and VGGnet. Experiments were performed on 1, 066 mammograms acquired with GE Senographe systems. Statistically significantly better microcalcification detection performance was obtained when dehazing was used as preprocessing. Results of dehazing were superior also to those obtained with Contrast Limited Adaptive Histogram Equalization (CLAHE).

Keywords

Spatial enhancement Dehazing Microcalcification detection Convolutional neural networks CAD 

Notes

Acknowledgment

The authors from University of Cassino gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan X Pascal GPUs.

References

  1. 1.
    Cancer Facts and Figures 2016. American Cancer Society (2016)Google Scholar
  2. 2.
    Bick, U., Diekmann, F.: Digital Mammography. Springer Science & Business Media, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Bria, A., Marrocco, C., Molinara, M., Tortorella, F.: An effective learning strategy for cascaded object detection. Inf. Sci. 340, 17–26 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bria, A., Marrocco, C., Karssemeijer, N., Molinara, M., Tortorella, F.: Deep cascade classifiers to detect clusters of microcalcifications. In: Tingberg, A., Lång, K., Timberg, P. (eds.) IWDM 2016. LNCS, vol. 9699, pp. 415–422. Springer, Cham (2016). doi: 10.1007/978-3-319-41546-8_52 Google Scholar
  5. 5.
    Bria, A., Marrocco, C., Molinara, M., Tortorella, F.: A ranking-based cascade approach for unbalanced data. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3439–3442. IEEE (2012)Google Scholar
  6. 6.
    Ciresan, D.C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, Barcelona, Spain, vol. 22, p. 1237 (2011)Google Scholar
  7. 7.
    Eadie, L.H., Taylor, P., Gibson, A.P.: A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. Eur. J. Radiol. 81(1), e70–e76 (2012)CrossRefGoogle Scholar
  8. 8.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, vol. 9, pp. 249–256 (2010)Google Scholar
  9. 9.
    Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)CrossRefGoogle Scholar
  10. 10.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  11. 11.
    He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRefGoogle Scholar
  12. 12.
    Jarrett, K., Kavukcuoglu, K., LeCun, Y., et al.: What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146–2153. IEEE (2009)Google Scholar
  13. 13.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  14. 14.
    Jing, H., Yang, Y., Nishikawa, R.M.: Detection of clustered microcalcifications using spatial point process modeling. Phys. Med. Biol. 56(1), 1–17 (2011)CrossRefGoogle Scholar
  15. 15.
    Koschmieder, H.: Theorie der horizontalen Sichtweite: Kontrast und Sichtweite. Keim & Nemnich (1925)Google Scholar
  16. 16.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  17. 17.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  18. 18.
    Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)CrossRefGoogle Scholar
  19. 19.
    Maitra, I.K., Nag, S., Bandyopadhyay, S.K.: Technique for preprocessing of digital mammogram. Comput. Methods Programs Biomed. 107(2), 175–188 (2012)CrossRefGoogle Scholar
  20. 20.
    Marchesi, A., Bria, A., Marrocco, C., Molinara, M., Mordang, J.J., Tortorella, F., Karssemeijer, N.: The effect of mammogram preprocessing on microcalcification detection with convolutional neural networks. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 118–121. IEEE (2017)Google Scholar
  21. 21.
    Marrocco, C., Molinara, M., Tortorella, F., Rinaldi, P., Bonomo, L., Ferrarotti, A., Aragno, C., lo Moriello, S.S.: Detection of cluster of microcalcifications based on watershed segmentation algorithm. In: 2012 25th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–5. IEEE (2012)Google Scholar
  22. 22.
    Mirzaalian, H., Ahmadzadeh, M.R., Sadri, S., Jafari, M.: Pre-processing algorithms on digital mammograms. In: MVA, pp. 118–121 (2007)Google Scholar
  23. 23.
    Molinara, M., Marrocco, C., Tortorella, F.: Automatic segmentation of the pectoral muscle in mediolateral oblique mammograms. In: 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), pp. 506–509. IEEE (2013)Google Scholar
  24. 24.
    Mordang, J.-J., Janssen, T., Bria, A., Kooi, T., Gubern-Mérida, A., Karssemeijer, N.: Automatic microcalcification detection in multi-vendor mammography using convolutional neural networks. In: Tingberg, A., Lång, K., Timberg, P. (eds.) IWDM 2016. LNCS, vol. 9699, pp. 35–42. Springer, Cham (2016). doi: 10.1007/978-3-319-41546-8_5 Google Scholar
  25. 25.
    Orlando, J.I., Prokofyeva, E., del Fresno, M., Blaschko, M.B.: Convolutional neural network transfer for automated glaucoma identification. In: 12th International Symposium on Medical Information Processing and Analysis, vol. 10160, p. 101600U (2017)Google Scholar
  26. 26.
    Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)CrossRefGoogle Scholar
  27. 27.
    Samuelson, F.W., Petrick, N.: Comparing image detection algorithms using resampling. In: International Symposium on Biomedical Imaging, pp. 1312–1315 (2006)Google Scholar
  28. 28.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  29. 29.
    Stomper, P.C., Geradts, J., Edge, S.B., Levine, E.G.: Mammographic predictors of the presence and size of invasive carcinomas associated with malignant microcalcification lesions without a mass. Am. J. Roentgenol. 181(6), 1679–1684 (2003)CrossRefGoogle Scholar
  30. 30.
    Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)CrossRefGoogle Scholar
  31. 31.
    Tarel, J.P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2201–2208. IEEE (2009)Google Scholar
  32. 32.
    Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., Li, L.: Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci. Rep. 6, 27327 (2016)CrossRefGoogle Scholar
  33. 33.
    Zeiler, M., Fergus, R.: Stochastic pooling for regularization of deep convolutional neural networks. In: Proceedings of the International Conference on Learning Representation (ICLR) (2013)Google Scholar
  34. 34.
    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV, pp. 474–485. Academic Press Professional, Inc. (1994)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alessandro Bria
    • 1
  • Claudio Marrocco
    • 1
  • Adrian Galdran
    • 2
  • Aurélio Campilho
    • 2
    • 3
  • Agnese Marchesi
    • 1
  • Jan-Jurre Mordang
    • 4
  • Nico Karssemeijer
    • 4
  • Mario Molinara
    • 1
  • Francesco Tortorella
    • 1
  1. 1.DIEIUniversity of Cassino and Southern LatiumCassinoItaly
  2. 2.INESC TEC, Institute for Systems and Computer Engineering, Technology and SciencePortoPortugal
  3. 3.Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  4. 4.DIAGRadboud University Nijmegen Medical CentreNijmegenThe Netherlands

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