6533b86dfe1ef96bd12c9675
RESEARCH PRODUCT
Morphological Analysis Combined with a Machine Learning Approach to Detect Utrasound Median Sagittal Sections for the Nuchal Translucency Measurement
Domenico TegoloCesare ValentiGiuseppa Sciortinosubject
Computer scienceSpeech recognition02 engineering and technologyWavelet analysi03 medical and health sciences0302 clinical medicineWaveletMid-sagittal section Neural network Nuchal translucency Symmetry transform Wavelet analysis.Nuchal translucencyRobustness (computer science)Nuchal Translucency Measurement0202 electrical engineering electronic engineering information engineeringmedicineMid-sagittal sectionSettore INF/01 - InformaticaArtificial neural networkbusiness.industrySymmetry transformPattern recognitionmedicine.diseaseNeural networkSagittal planemedicine.anatomical_structureNuchal translucencyMorphological analysis020201 artificial intelligence & image processingArtificial intelligenceTrisomybusiness030217 neurology & neurosurgerydescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 |