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B-Mode Ultrasound Breast Anatomy Segmentation

  • João F. TeixeiraEmail author
  • António M. Carreiro
  • Rute M. Santos
  • Hélder P. Oliveira
Conference paper
  • 164 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12132)

Abstract

Breast Ultrasound has long been used to support diagnostic and exploratory procedures concerning breast cancer, with an interesting success rate, specially when complemented with other radiology information. This usability can further enhance visualization tasks during pre-treatment clinical analysis by coupling the B-Mode images to 3D space, as found in Magnetic Resonance Imaging (MRI) per instance. In fact, Lesions in B-mode are visible and present high detail when comparing with other 3D sequences. This coupling, however, would be largely benefited from the ability to match the various structures present in the B-Mode, apart from the broadly studied lesion. In this work we focus on structures such as skin, subcutaneous fat, mammary gland and thoracic region. We provide a preliminary insight to several structure segmentation approaches in the hopes of obtaining a functional and dependable pipeline for delineating these potential reference regions that will assist in multi-modal radiological data alignment. For this, we experiment with pre-processing stages that include Anisotropic Diffusion guided by Log-Gabor filters (ADLG) and main segmentation steps using K-Means, Meanshift and Watershed.

Among the pipeline configurations tested, the best results were found using the ADLG filter that ran for 50 iterations and H-Maxima suppression of 20% and the K-Means method with \(K=6\). The results present several cases that closely approach the ground truth despite overall having larger average errors. This encourages the experimentation of other approaches that could withstand the innate data variability that makes this task very challenging.

Keywords

Segmentation Rigid registration Minimum path B-mode Ultrasound Anatomical structures Breast cancer 

References

  1. 1.
    Ahmad, S., Bolic, M., Dajani, H., Groza, V., Batkin, I., Rajan, S.: Measurement of heart rate variability using an oscillometric blood pressure monitor. IEEE Trans. Instrum. Meas. 59(10), 2575–2590 (2010).  https://doi.org/10.1109/TIM.2010.2057571CrossRefGoogle Scholar
  2. 2.
    Aiazzi, B., Alparone, L., Baronti, S.: Multiresolution local-statistics speckle filtering based on a ratio Laplacian pyramid. IEEE Trans. Geosci. Remote Sens. 36(5), 1466–1476 (1998).  https://doi.org/10.1109/36.718850CrossRefGoogle Scholar
  3. 3.
    Arthur, D., Vassilvitskii, S.: k-Means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)Google Scholar
  4. 4.
    Boukerroui, D., Baskurt, A., Noble, J.A., Basset, O.: Segmentation of ultrasound images - multiresolution 2D and 3D algorithm based on global and local statistics. Pattern Recogn. Lett. 24(4), 779–790 (2003).  https://doi.org/10.1016/S0167-8655(02)00181-2CrossRefGoogle Scholar
  5. 5.
    Braz, R., Pinheiro, A.M.G., Moutinho, J., Freire, M.M., Pereira, M.: Breast ultrasound images gland segmentation. In: 2012 IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6, September 2012.  https://doi.org/10.1109/MLSP.2012.6349748
  6. 6.
    Cardoso, F.M., Matsumoto, M.M.S., Furuie, S.S.: Edge-preserving speckle texture removal by interference-based speckle filtering followed by anisotropic diffusion. Ultrasound Med. Biol. 38(8), 1414–1428 (2012).  https://doi.org/10.1016/j.ultrasmedbio.2012.03.014CrossRefGoogle Scholar
  7. 7.
    Coupe, P., Hellier, P., Kervrann, C., Barillot, C.: Bayesian non local means-based speckle filtering. In: 2008 5th IEEE International Symposium on Biomedical Imaging From Nano to Macro, pp. 1291–1294, May 2008.  https://doi.org/10.1109/ISBI.2008.4541240
  8. 8.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945).  https://doi.org/10.2307/1932409CrossRefGoogle Scholar
  9. 9.
    Flores, W.G., Pereira, W.C.A., Infantosi, A.F.C.: Breast ultrasound despeckling using anisotropic diffusion guided by texture descriptors. Ultrasound Med. Biol. 40(11), 2609–2621 (2014).  https://doi.org/10.1016/j.ultrasmedbio.2014.06.005CrossRefGoogle Scholar
  10. 10.
    Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975).  https://doi.org/10.1109/tit.1975.1055330MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Gómez, W., Leija, L., Alvarenga, A.V., Infantosi, A.F.C., Pereira, W.C.A.: Computerized lesion segmentation of breast ultrasound based on marker-controlled watershed transformation. Med. Phys. 37(1), 82–95 (2010).  https://doi.org/10.1118/1.3265959CrossRefGoogle Scholar
  12. 12.
    Guo, Y., Wang, Y., Hou, T.: Speckle filtering of ultrasonic images using a modified non local-based algorithm. Biomed. Signal Process. Control 6(2), 129–138 (2011).  https://doi.org/10.1016/j.bspc.2010.10.004. Special Issue: The Advance of Signal Processing for BioelectronicsCrossRefGoogle Scholar
  13. 13.
    Hassan, M., Chaudhry, A., Khan, A., Iftikhar, M.A., Kim, J.Y.: Medical image segmentation employing information gain and fuzzy c-means algorithm. In: 2013 International Conference on Open Source Systems and Technologies, pp. 34–39, December 2013.  https://doi.org/10.1109/ICOSST.2013.6720602
  14. 14.
    Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Trans. Acoust. Speech Signal Process. 27(1), 13–18 (1979).  https://doi.org/10.1109/TASSP.1979.1163188CrossRefGoogle Scholar
  15. 15.
    Jameson, J.L., et al.: Harrison’s Principles of Internal Medicine. McGraw-Hill Education / Medical, New York (2018). OCLC: 990065894Google Scholar
  16. 16.
    K. D. Marcomini, A.A.O.C., Schiabel, H.: Application of artificial neural network models in segmentation and classification of nodules in breast ultrasound digital images. Int. J. Biomed. Imaging 13 (2016).  https://doi.org/10.1155/2016/7987212
  17. 17.
    Meyer, F.: Topographic distance and watershed lines. Sig. Process. 38(1), 113–125 (1994).  https://doi.org/10.1016/0165-1684(94)90060-4CrossRefzbMATHGoogle Scholar
  18. 18.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).  https://doi.org/10.1109/TSMC.1979.4310076CrossRefGoogle Scholar
  19. 19.
    Shan, J., Cheng, H.D., Wang, Y.: A novel automatic seed point selection algorithm for breast ultrasound images. In: 2008 19th International Conference on Pattern Recognition, pp. 1–4, December 2008.  https://doi.org/10.1109/ICPR.2008.4761336
  20. 20.
    Sørensen, T.J.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948)Google Scholar
  21. 21.
    Taha, A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1) (2015).  https://doi.org/10.1186/s12880-015-0068-x
  22. 22.
    Thijssen, J.M.: Ultrasonic speckle formation, analysis and processing applied to tissue characterization. Pattern Recogn. Lett. 24(4), 659–675 (2003).  https://doi.org/10.1016/S0167-8655(02)00173-3CrossRefGoogle Scholar
  23. 23.
    Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11(11), 1260–1270 (2002).  https://doi.org/10.1109/TIP.2002.804276MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Engineering of the University of PortoPortoPortugal
  2. 2.Faculty of Sciences of the University of PortoPortoPortugal
  3. 3.INESC TECPortoPortugal
  4. 4.Escola Superior de Tecnologia da Saúde de CoimbraCoimbraPortugal

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