Affiliations: [a] University of Tunis, ENSIT, LR13ES03 SIME, Tunis, Tunisia | [b] University of Tunis El Manar, ISTMT, LR13ES07, LRBTM, Tunis, Tunisia | [c] Obstetrics, Gynecology and Reproductive Department, Military Hospital, Tunis, Tunisia
Correspondence:
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Corresponding author: Hanene Sahli, University of Tunis, The National Higher School of Engineers of Tunis, Laboratory of Signal Image and Energy Mastery, 5 Avenue Taha Hussein, 1008 Tunis, Tunisia. E-mail: [email protected].
Abstract: BACKGROUD: Hydrocephalus is the most common anomaly of the fetal head characterized by an excessive accumulation of fluid in the brain processing. The diagnostic process of fetal heads using traditional evaluation techniques are generally time consuming and error prone. Usually, fetal head size is computed using an ultrasound (US) image around 20–22 weeks, which is the gestational age (GA). Biometrical measurements are extracted and compared with ground truth charts to identify normal or abnormal growth. METHODS: In this paper, an attempt has been made to enhance the Hydrocephalus characterization process by extracting other geometrical and textural features to design an efficient recognition system. The superiority of this work consists of the reduced time processing and the complexity of standard automatic approaches for routine examination. This proposed method requires practical insidiousness of the precocious discovery of fetuses’ malformation to alert the experts about the existence of abnormal outcome. The first task is devoted to a proposed pre-processing model using a standard filtering and a segmentation scheme using a modified Hough transform (MHT) to detect the region of interest. Indeed, the obtained clinical parameters are presented to the principal component analysis (PCA) model in order to obtain a reduced number of measures which are employed in the classification stage. RESULTS: Thanks to the combination of geometrical and statistical features, the classification process provided an important ability and an interesting performance achieving more than 96% of accuracy to detect pathological subjects in premature ages. CONCLUSIONS: The experimental results illustrate the success and the accuracy of the proposed classification method for a factual diagnostic of fetal head malformation.
Keywords: Hydrocephalus, fetal US imaging, fetal head biometry, modified Hough transform (MHT), feature extraction
