The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 88-119
Full Name: Lai Khin Wee
Position: Researcher
Age: ON
Sex: Male
Address: FKBSK, Universiti Teknologi Malaysia
Country: MALAYSIA
Tel: 0049-15774853251
Tel prefix:
Fax:
E-mail address: laikw2@gmail.com
Other E-mails: khinwee@yahoo.com, eko@utm.my, khinwee@daad-alumni.de
Title of the Paper: Nuchal Translucency Marker Detection Based on Artificial Neural Network and Measurement via Bidirectional Iteration Forward Propagation
Authors as they appear in the Paper: Lai Khin Wee, Too Yuen Min, Adeela Arooj, Eko Supriyanto
Email addresses of all the authors: laikw2@gmail.com, eko@utm.my, khinwee@daad-alumni.de
Number of paper pages: 12
Abstract: Ultrasound screening is performed during early pregnancy for assessment of fetal viability and prenatal diagnosis of fetal chromosomal anomalies including measurement of nuchal translucency (NT) thickness. The drawback of current NT measurement technique is restricted with inter and intra-observer variability and inconsistency of results. Hence, we present an automated detection and measurement method for NT in this study. Artificial neural network was trained to locate the region of interest (ROI) that contains NT. The accuracy of the trained network was achieved at least 93.33 percent which promise an efficient method to recognize NT automatically. Border of NT layer was detected through automatic computerized algorithm to find the optimum thickness of the windowed region. Local measurements of intensity, edge strength and continuity were extracted and became the weighted terms for thickness calculation. Finding showed that this method is able to provide consiste!
nt and more objective results.
Keywords: nuchal translucency, ultrasound, fetal, pattern recognition, artificial neural network
EXTENSION of the file: .pdf
Special (Invited) Session: Ultrasonic Marker Pattern Recognition and Measurement Using Artificial Neural Network
Organizer of the Session: SIP-05 or 645-164
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