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Texture-Based Filtering and Front-Propagation Techniques for the Segmentation of Ultrasound Images

  • Miguel Alemán-Flores
  • Patricia Alemán-Flores
  • Luis Álvarez-León
  • M. Belén Esteban-Sánchez
  • Rafael Fuentes-Pavón
  • José M. Santana-Montesdeoca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4739)

Abstract

Ultrasound imaging segmentation is a common method used to help in the diagnosis in multiple medical disciplines. This medical image modality is particularly difficult to segment and analyze since the quality of the images is relatively low, because of the presence of speckle noise. In this paper we present a set of techniques, based on texture findings, to increase the quality of the images. We characterize the ultrasound image texture by a vector of responses to a set of Gabor filters. Also, we combine front-propagation and active contours segmentation methods to achieve a fast accurate segmentation with the minimal expert intervention.

Keywords

Ultrasound Image Active Contour Image Texture Gabor Filter Gradient Magnitude 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Erikson, K.R., Fry, F.J., Jones, J.P.: Ultrasound in medicine-a review. IEEE Transactions on Sonics and Ultrasonics 21(3), 144–170 (1974)Google Scholar
  2. 2.
    Stavros, A.T., Thickman, D., Rapp, C.L., Dennis, M.A., Parker, S.H., Sisney, G.A.: Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology 196, 123–134 (1995)Google Scholar
  3. 3.
    Davies, E.R.: On the noise suppression and image enhancement characteristics of the median, truncated median and mode filters. Pattern Recogn. Lett. 7(2), 87–97 (1988)CrossRefGoogle Scholar
  4. 4.
    Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  5. 5.
    Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 11(11), 1260–1270 (2002)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Voci, F., Eiho, S., Sugimoto, N., Sekibuchi, H.: Estimating the gradient in the perona-malik equation. IEEE Signal Processing Magazine 21(3), 39–65 (2004)CrossRefGoogle Scholar
  7. 7.
    Daugman, J.G.: Complete discrete 2-d gabor transforms by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech and Signal Processing 36(7), 1169–1179 (1988)zbMATHCrossRefGoogle Scholar
  8. 8.
    Bovik, A.C., Clark, M., Geisler, W.S.: Multichannel texture analysis using localized spatial filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 55–73 (1990)CrossRefGoogle Scholar
  9. 9.
    Weldon, T.P., Higgins, W.E.: Design of Multiple Gabor Filters for Texture Segmentation. vol. 4, pp. 2243–2246 (1996)Google Scholar
  10. 10.
    Dunn, D., Higgings, W.E.: Optimal gabor filters for texture segmentation. IEEE Transactions on Image Processing 4(7), 947–964 (1995)CrossRefGoogle Scholar
  11. 11.
    Anil, K., Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recogn. 24(12), 1167–1186 (1990)Google Scholar
  12. 12.
    Mohamed, S.S., Abdel-galil, T.K., Salama, M.M.A., Fenster, A., Rizkalla, K., Downey, D.B.: Prostate cancer diagnosis based on gabor filter texture segmentation of ultrasound image. In: CCECE 2003, vol. 3, pp. 1485–1488 (2003)Google Scholar
  13. 13.
    Xie, J., Jiang, Y., Hung-Tat, T.: Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE transactions on medical imaging 24, 45–57 (2005)CrossRefGoogle Scholar
  14. 14.
    Gabor, D.: Theory of communication. Journ. of Inst. Electrical Engineers 93(26), 429–457 (1946)Google Scholar
  15. 15.
    De Valois, R.L., De Valois, K.K.: Spatial Vision. Oxford University Press, Oxford (1988)Google Scholar
  16. 16.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. In: ICCV, pp. 694–699 (1995)Google Scholar
  17. 17.
    Sato, M., Lakare, S., Wan, M., Kaufman, A., Nakajima, M.: A gradient magnitude based region growing algorithm for accurate segmentation. In: Proceedings of the International Conference on Image Processing, vol. 3, pp. 448–451 (2000)Google Scholar
  18. 18.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Miguel Alemán-Flores
    • 1
  • Patricia Alemán-Flores
    • 2
  • Luis Álvarez-León
    • 1
  • M. Belén Esteban-Sánchez
    • 1
  • Rafael Fuentes-Pavón
    • 2
  • José M. Santana-Montesdeoca
    • 2
  1. 1.Departamento de Informática y Sistemas, Universidad de Las Palmas de Gran Canaria, 35017, Las PalmasSpain
  2. 2.Sección de Ecografía, Servicio de Radiodiagnóstico, Hospital Universitario Insular de Gran Canaria, 35016, Las PalmasSpain

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