AUTOMATIC FETAL HEAD CANDIDATE LOCALIZATION FROM 2D ULTRASOUND IMAGES USING HAAR CASCADE CLASSIFIER AND ENHANCED LOCALIZATION ALGORITHM

• 主要作者: Rismonita, Tessya
• 格式: Final Project
• 語言: Indonesia
• 在線閱讀: https://digilib.itb.ac.id/gdl/view/48388
• 機構: Institut Teknologi Bandung

Fetal head circumference (HC) is one of the fetal biometrics that is often used to determine gestational age and monitor the fetal growth in the womb. Nowadays, head circumference measurement from ultrasonography images is performed manually by a doctor or sonographer by drawing a line or forming an ellipse to surround the fetal head. However, manual annotations are prone to human error and intra-observer as well as inter-observer variabilities. In this final project, an automatic fetal head candidate localization was implemented using Haar Cascade Classifier (HCC) and further optimized by three types of Enhanced Localization Algorithm (ELA), namely ELA I, ELA II, and ELA III. The three combinations of HCC and each respective ELA were evaluated on 703 ultrasound images of the second trimester and 141 ultrasound images of the third trimester using the Jaccard Index (JI), Dice Similarity Coefficient (DSC), and Overlapped Area Ratio (OAR). The best localization results were performed by the HCC + ELA III with an average JI of 90.5%, DSC of 94.58%, OAR of 97.77% for the second trimester and an average JI of 88.17%, DSC of 93.33%, OAR of 96.97% for the third trimester. Based on the three evaluation parameters, researcher analyzed the factors affecting the accuracy of the localization algorithm and the correspondence of the localization results with the ellipse fitting outcome as the final process to determine the fetal head circumference.