Morphological analysis combined with a machine learning approach to detect utrasound median sagittal sections for the nuchal translucency measurement

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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.
Lingua originaleEnglish
Titolo della pubblicazione ospitePattern Recognition, 9th Mexican Conference, MCPR 2017, Huatulco, Mexico, June 21-24, 2017, Proceedings
Pagine257-267
Numero di pagine11
Stato di pubblicazionePublished - 2017

Serie di pubblicazioni

NomeLECTURE NOTES IN COMPUTER SCIENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
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