Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images
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, especial...
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Published in | Journal of medical engineering & technology Vol. 43; no. 5; pp. 279 - 286 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
England
Taylor & Francis
04.07.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0309-1902 1464-522X 1464-522X |
DOI: | 10.1080/03091902.2019.1653389 |