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)


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.


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


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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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