Ultrasound imaging is a well known noninvasive way to evaluate various diseases during the prenatal age. In particular, the thickness measure of the nuchal transucency is strictly correlated with pathologies like trisomy 13, 18 and 21. For a correct investigation, the methodology needs mid-sagittal sections and the proposed approach is based on wavelet analysis and neural network classifiers to locate components useful to identify mid-sagittal planes. To evaluate the performance and the robustness of the methodology, real clinical ultrasound images were considered, obtaining an average error of at most 0.3 millimeters in 97.4% of the cases

Sciortino, G., Tegolo, D., Valenti, C. (2017). A non-supervised approach to locate and to measure the nuchal translucency by means of wavelet analysis and neural networks. In ICAT 2017 - 26th International Conference on Information, Communication and Automation Technologies, Proceedings (pp. 1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICAT.2017.8171631].

A non-supervised approach to locate and to measure the nuchal translucency by means of wavelet analysis and neural networks

Sciortino, Giuseppa
;
Tegolo, Domenico
;
Valenti, Cesare
2017-01-01

Abstract

Ultrasound imaging is a well known noninvasive way to evaluate various diseases during the prenatal age. In particular, the thickness measure of the nuchal transucency is strictly correlated with pathologies like trisomy 13, 18 and 21. For a correct investigation, the methodology needs mid-sagittal sections and the proposed approach is based on wavelet analysis and neural network classifiers to locate components useful to identify mid-sagittal planes. To evaluate the performance and the robustness of the methodology, real clinical ultrasound images were considered, obtaining an average error of at most 0.3 millimeters in 97.4% of the cases
2017
Settore INF/01 - Informatica
978-1-5386-3337-3
Sciortino, G., Tegolo, D., Valenti, C. (2017). A non-supervised approach to locate and to measure the nuchal translucency by means of wavelet analysis and neural networks. In ICAT 2017 - 26th International Conference on Information, Communication and Automation Technologies, Proceedings (pp. 1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICAT.2017.8171631].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/289995
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