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2017 | OriginalPaper | Buchkapitel

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

verfasst von : Giuseppa Sciortino, Domenico Tegolo, Cesare Valenti

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

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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.

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Titel: Morphological Analysis Combined with a Machine Learning Approach to Detect Utrasound Median Sagittal Sections for the Nuchal Translucency Measurement
verfasst von: Giuseppa Sciortino
Domenico Tegolo
Cesare Valenti
Verlag: Springer International Publishing
Buch: Pattern Recognition
Print ISBN: 978-3-319-59225-1

Electronic ISBN: 978-3-319-59226-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59226-8_25

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