Search results for "Clinical ultrasound"

showing 2 items of 2 documents

Automatic detection and measurement of nuchal translucency.

2017

In this paper we propose a new methodology to support the physician both to identify automatically the nuchal region and to obtain a correct thickness measurement of the nuchal translucency. The thickness of the nuchal translucency is one of the main markers for screening of chromosomal defects such as trisomy 13, 18 and 21. Its measurement is performed during ultrasound scanning in the first trimester of pregnancy. The proposed methodology is mainly based on wavelet and multi resolution analysis. The performance of our method was analysed on 382 random frames, representing mid-sagittal sections, uniformly extracted from real clinical ultrasound videos of 12 patients. According to the groun…

0301 basic medicinemedicine.medical_specialtyWavelet AnalysisFirst trimester of pregnancyHealth InformaticsSensitivity and SpecificityWavelet analysi030218 nuclear medicine & medical imagingPattern Recognition AutomatedMachine Learning03 medical and health sciencesPrenatal ultrasound0302 clinical medicineNuchal regionNuchal translucencyUltrasound fetal examinationMedian sagittal sectionNuchal Translucency MeasurementImage Interpretation Computer-AssistedMedicineHumansPixelbusiness.industryMulti resolution analysisUltrasoundReproducibility of ResultsPattern recognitionComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science ApplicationsSurgeryClinical ultrasound030104 developmental biologyNuchal translucencyArtificial intelligenceDown SyndromebusinessNuchal Translucency MeasurementAlgorithmsComputers in biology and medicine
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A non-supervised approach to locate and to measure the nuchal translucency by means of wavelet analysis and neural networks

2017

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.

Control and OptimizationArtificial neural networkSettore INF/01 - InformaticaComputer sciencebusiness.industrymid-sagittal sectionneural networksymmetry transformPattern recognitionMeasure (mathematics)Ultrasonic imagingClinical ultrasoundWaveletComputer Networks and CommunicationNuchal translucencyRobustness (computer science)Artificial IntelligenceUltrasound imagingArtificial intelligencewavelet analysibusinessnuchal translucencyInformation Systems
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