Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images.
Identifieur interne : 000603 ( Main/Exploration ); précédent : 000602; suivant : 000604Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images.
Auteurs : Hanene Sahli [Tunisie] ; Aymen Mouelhi [Tunisie] ; Amine Ben Slama [Tunisie] ; Mounir Sayadi [Tunisie] ; Radhouane Rachdi [Tunisie]Source :
- Journal of medical engineering & technology [ 1464-522X ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- imagerie diagnostique : Tête.
- malformations : Encéphale.
- Bases de données factuelles, Biométrie, Diagnostic prénatal, Foetus, Humains, Machine à vecteur de support, Traitement d'image par ordinateur, Âge gestationnel, Échographie.
English descriptors
- KwdEn :
- MESH :
- abnormalities : Brain.
- diagnostic imaging : Head.
- Biometry, Databases, Factual, Fetus, Gestational Age, Humans, Image Processing, Computer-Assisted, Prenatal Diagnosis, Support Vector Machine, Ultrasonography.
Abstract
This paper presents an advanced approach for foetal brain abnormalities diagnostic by integrating significant biometric features in the identification process. In foetal anomaly diagnosis, manual evaluation of foetal behaviour in ultrasound images is a subjective, slow and error-prone task, especially in the preliminary treatment phases. The effectiveness of this appearance is strictly subject to the attention and the experience of gynaecologists. In this case, automatic methods of image analysis offer the possibility of obtaining a homogeneous, objective and above all fast diagnosis of the foetal head in order to identify pregnancy behaviour. Indeed, we propose a computerised diagnostic method based on morphological characteristics and a supervised classification method to categorise subjects into two groups: normal and affected cases. The presented method is validated on a real integrated microcephaly and dolichocephaly cases. The studied database contains the same gestational age of both normal and abnormal foetuses. The results show that the use of a support vector machine (SVM) classifier is an effective way to enhance recognition and detection for rapid and accurate foetal head diagnostic.
DOI: 10.1080/03091902.2019.1653389
PubMed: 31502902
Affiliations:
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Le document en format XML
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Sahli</name><affiliation wicri:level="1"><nlm:affiliation>University of Tunis, ENSIT, LR13ES03, Signal Image and Energy Mastery, Montfleury , Tunis , Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>University of Tunis, ENSIT, LR13ES03, Signal Image and Energy Mastery, Montfleury , Tunis </wicri:regionArea><wicri:noRegion>Tunis </wicri:noRegion></affiliation></author><author><name sortKey="Mouelhi, Aymen" sort="Mouelhi, Aymen" uniqKey="Mouelhi A" first="Aymen" last="Mouelhi">Aymen Mouelhi</name><affiliation wicri:level="1"><nlm:affiliation>University of Tunis, ENSIT, LR13ES03, Signal Image and Energy Mastery, Montfleury , Tunis , Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>University of Tunis, ENSIT, LR13ES03, Signal Image and Energy Mastery, Montfleury , Tunis </wicri:regionArea><wicri:noRegion>Tunis </wicri:noRegion></affiliation></author><author><name sortKey="Ben Slama, Amine" sort="Ben Slama, Amine" uniqKey="Ben Slama A" first="Amine" last="Ben Slama">Amine Ben Slama</name><affiliation wicri:level="1"><nlm:affiliation>University of Tunis El-Manar, ISTMT, LR13ES07, Laboratory of Biophysics and Medical Technologies , Tunis , Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>University of Tunis El-Manar, ISTMT, LR13ES07, Laboratory of Biophysics and Medical Technologies , Tunis </wicri:regionArea><wicri:noRegion>Tunis </wicri:noRegion></affiliation></author><author><name sortKey="Sayadi, Mounir" sort="Sayadi, Mounir" uniqKey="Sayadi M" first="Mounir" last="Sayadi">Mounir Sayadi</name><affiliation wicri:level="1"><nlm:affiliation>University of Tunis, ENSIT, LR13ES03, Signal Image and Energy Mastery, Montfleury , Tunis , Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>University of Tunis, ENSIT, LR13ES03, Signal Image and Energy Mastery, Montfleury , Tunis </wicri:regionArea><wicri:noRegion>Tunis </wicri:noRegion></affiliation></author><author><name sortKey="Rachdi, Radhouane" sort="Rachdi, Radhouane" uniqKey="Rachdi R" first="Radhouane" last="Rachdi">Radhouane Rachdi</name><affiliation wicri:level="1"><nlm:affiliation>Department of Maternity, Military Hospital , Tunis , Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>Department of Maternity, Military Hospital , Tunis </wicri:regionArea><wicri:noRegion>Tunis </wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of medical engineering & technology</title><idno type="eISSN">1464-522X</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Biometry (MeSH)</term><term>Brain (abnormalities)</term><term>Databases, Factual (MeSH)</term><term>Fetus (MeSH)</term><term>Gestational Age (MeSH)</term><term>Head (diagnostic imaging)</term><term>Humans (MeSH)</term><term>Image Processing, Computer-Assisted (MeSH)</term><term>Prenatal Diagnosis (MeSH)</term><term>Support Vector Machine (MeSH)</term><term>Ultrasonography (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Bases de données factuelles (MeSH)</term><term>Biométrie (MeSH)</term><term>Diagnostic prénatal (MeSH)</term><term>Encéphale (malformations)</term><term>Foetus (MeSH)</term><term>Humains (MeSH)</term><term>Machine à vecteur de support (MeSH)</term><term>Traitement d'image par ordinateur (MeSH)</term><term>Tête (imagerie diagnostique)</term><term>Âge gestationnel (MeSH)</term><term>Échographie (MeSH)</term></keywords><keywords scheme="MESH" qualifier="abnormalities" xml:lang="en"><term>Brain</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Head</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Tête</term></keywords><keywords scheme="MESH" qualifier="malformations" xml:lang="fr"><term>Encéphale</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Biometry</term><term>Databases, Factual</term><term>Fetus</term><term>Gestational Age</term><term>Humans</term><term>Image Processing, Computer-Assisted</term><term>Prenatal Diagnosis</term><term>Support Vector Machine</term><term>Ultrasonography</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Bases de données factuelles</term><term>Biométrie</term><term>Diagnostic prénatal</term><term>Foetus</term><term>Humains</term><term>Machine à vecteur de support</term><term>Traitement d'image par ordinateur</term><term>Âge gestationnel</term><term>Échographie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper presents an advanced approach for foetal brain abnormalities diagnostic by integrating significant biometric features in the identification process. In foetal anomaly diagnosis, manual evaluation of foetal behaviour in ultrasound images is a subjective, slow and error-prone task, especially in the preliminary treatment phases. The effectiveness of this appearance is strictly subject to the attention and the experience of gynaecologists. In this case, automatic methods of image analysis offer the possibility of obtaining a homogeneous, objective and above all fast diagnosis of the foetal head in order to identify pregnancy behaviour. Indeed, we propose a computerised diagnostic method based on morphological characteristics and a supervised classification method to categorise subjects into two groups: normal and affected cases. The presented method is validated on a real integrated microcephaly and dolichocephaly cases. The studied database contains the same gestational age of both normal and abnormal foetuses. The results show that the use of a support vector machine (SVM) classifier is an effective way to enhance recognition and detection for rapid and accurate foetal head diagnostic.</div></front></TEI><affiliations><list><country><li>Tunisie</li></country></list><tree><country name="Tunisie"><noRegion><name sortKey="Sahli, Hanene" sort="Sahli, Hanene" uniqKey="Sahli H" first="Hanene" last="Sahli">Hanene Sahli</name></noRegion><name sortKey="Ben Slama, Amine" sort="Ben Slama, Amine" uniqKey="Ben Slama A" first="Amine" last="Ben Slama">Amine Ben Slama</name><name sortKey="Mouelhi, Aymen" sort="Mouelhi, Aymen" uniqKey="Mouelhi A" first="Aymen" last="Mouelhi">Aymen Mouelhi</name><name sortKey="Rachdi, Radhouane" sort="Rachdi, Radhouane" uniqKey="Rachdi R" first="Radhouane" last="Rachdi">Radhouane Rachdi</name><name sortKey="Sayadi, Mounir" sort="Sayadi, Mounir" uniqKey="Sayadi M" first="Mounir" last="Sayadi">Mounir Sayadi</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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