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Morphological Analysis Combined with a Machine Learning Approach to Detect Utrasound Median Sagittal Sections for the Nuchal Translucency Measurement

  • Giuseppa Sciortino
  • Domenico TegoloEmail author
  • Cesare Valenti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)

Abstract

The screening of chromosomal defects, as trisomy 13, 18 and 21, can be obtained by the measurement of the nuchal translucency thickness scanning during the end of the first trimester of pregnancy. This contribution proposes an automatic methodology to detect mid-sagittal sections to identify the correct measurement of nuchal translucency. Wavelet analysis and neural network classifiers are the main strategies of the proposed methodology to detect the frontal components of the skull and the choroid plexus with the support of radial symmetry analysis. Real clinical ultrasound images were adopted to measure the performance and the robustness of the methodology, thus it can be highlighted an error of at most 0.3 mm in 97.4% of the cases.

Keywords

Mid-sagittal section Neural network Nuchal translucency Symmetry transform Wavelet analysis 

References

  1. 1.
    Alfirevic, Z., Sundberg, K., Mujezinovic, F.: Amniocentesis and chorionic villus sampling for prenatal diagnosis. Cochrane Database Syst. Rev. 3 (2003). doi: 10.1002/14651858.CD003252
  2. 2.
    Anzalone, A., Fusco, G., Isgrò, F., Orlandi, E., Prevete, R., Sciortino, G., Tegolo, D., Valenti, C.: A system for the automatic measurement of the nuchal translucency thickness from ultrasound video stream of the foetus. In: International Symposium on Computer-Based Medical Systems, pp. 239–244. IEEE (2013)Google Scholar
  3. 3.
    Bernardino, F., Cardoso, R., Montenegro, N., Bernardes, J., Marques De Sà, J.: Semiautomated ultrasonographic measurement of fetal nuchal translucency using a computer software tool. Ultrasound Med. Biol. 24(1), 51–54 (1998)CrossRefGoogle Scholar
  4. 4.
    Catanzariti, E., Fusco, G., Isgrò, F., Masecchia, S., Prevete, R., Santoro, M.: A semi-automated method for the measurement of the fetal nuchal translucency in ultrasound images. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 613–622. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04146-4_66 CrossRefGoogle Scholar
  5. 5.
    Deng, Y., Wang, Y., Chen, P.: Automated detection of fetal nuchal translucency based on hierarchical structural model. In: International Symposium on Computer-Based Medical Systems, pp. 78–84. IEEE (2010)Google Scholar
  6. 6.
    Egmont-Petersen, M., Ridder, D., Handels, H.: Image processing with neural networks - a review. Pattern Recogn. 35(10), 2279–2301 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    FMF. Fetal Medicine Foundation nuchal translucency. www.fetalmedicine.org
  8. 8.
    González-Audícana, M., Otazu, X., Fors, O., Seco, A.: Comparison between Mallat’s and the ‘à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. Int. J. Remote Sens. 26(3), 595–614 (2005)CrossRefGoogle Scholar
  9. 9.
    Guastella, D., Valenti, C.: Cartoon filter via adaptive abstraction. J. Visual Commun. Image Represent. 36, 149–158 (2016)CrossRefGoogle Scholar
  10. 10.
    Jain, R., Kasturi, R., Schunck, B.: Machine Vision. McGraw-Hill, New York (1995)Google Scholar
  11. 11.
    Lee, Y., Kim, M., Kim, M.: Robust border enhancement and detection for measurement of fetal nuchal translucency in ultrasound images. Med. Biol. Eng. Comput. 45(11), 1143–1152 (2007)CrossRefGoogle Scholar
  12. 12.
    Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 959–973 (2003)CrossRefzbMATHGoogle Scholar
  13. 13.
    Orlandi, F., Rossi, C., Orlandi, E., Jakil, M., Hallahan, T., Macri, V., Krantz, D.: First-trimester screening for trisomy-21 using a simplified method to assess the presence or absence of the fetal nasal bone. Am. J. Obstet. Gynecol. 194(4), 1107–1111 (2005)CrossRefGoogle Scholar
  14. 14.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  15. 15.
    Sciortino, G., Orlandi, E., Valenti, C., Tegolo, D.: Wavelet analysis and neural network classifiers to detect mid-sagittal sections for nuchal translucency measurement. Image Anal. Stereology 35(2), 105–115 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Sciortino, G., Tegolo, D., Valenti, C.: Automatic detection and measurement of nuchal translucency. Comput. Biol. Med. 82, 12–20 (2017)CrossRefGoogle Scholar
  17. 17.
    Shensa, M.: The discrete wavelet transform: wedding the à trous and Mallat algorithms. IEEE Trans. Sig. Process 40(10), 2464–2482 (1992)CrossRefzbMATHGoogle Scholar
  18. 18.
    Snijders, R., Noble, P., Sebire, N., Souka, A., Nicolaides, K.: UK multicentre project on assessment of risk of trisomy 21 by maternal age and fetal nuchal translucency thickness at 1014 weeks of gestation. Lancet 6(9125), 343–351 (1998)CrossRefGoogle Scholar
  19. 19.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  20. 20.
    Wald, N., George, L., Smith, D., Densem, J., Pettersonm, K.: Serum screening for Down’s syndrome between 8 and 14 weeks of pregnancy. Br. J. Obstet. Gynecol. 103(5), 407–412 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Giuseppa Sciortino
    • 1
  • Domenico Tegolo
    • 1
    • 2
    Email author
  • Cesare Valenti
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità degli Studi di PalermoPalermoItaly
  2. 2.CHAB-Mediterranean Center for Human Health Advanced BiotechnologiesPalermoItaly

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