The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 88-136
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: Computerized Automatic Nasal Bone Detection based on Ultrasound Fetal Images Using Cross Correlation Techniques
Authors as they appear in the Paper: Lai Khin Wee, Adeela Arooj, Eko Supriyanto
Email addresses of all the authors: laikw2@gmail.com, eko@utm.my, khinwee@daad-alumni.de
Number of paper pages: 10
Abstract: Recent study shows that fetal chromosomal abnormalities can be detected in early ultrasonic prenatal screening by identifying the absence of fetus nasal bone. The drawbacks of current method are operator dependent, observer variability and improper training. In particular, accurate nasal bone detection requires highly trained sonographers, obstetricians and fetal medicine specialists since the ultrasound markers may easily confuse with noise and echogenic line in ultrasound image background. We present a computerized method of detecting the absence of nasal bone by using normalized grayscale cross correlation techniques. Image preprocessing is implemented prior to cross correlation to assess the availability of nasal bone. The resultant threshold, bordering the absence and presence of nasal bone, is set to a value of 0.35. The accuracy of the developed algorithm achieved was, around 96.26 percent which promises an efficient method to recognize nasal bone automatic!
ally. The threshold can be further improved if a larger set of nasal bone ultrasound images are applied.
Keywords: Chromosomal abnormalities, nasal bone, cross correlation, ultrasound image, matching, trisomy
EXTENSION of the file: .pdf
Special (Invited) Session: Automatic Detection of Fetal Nasal Bone in 2 Dimensional Ultrasound Image Using Map Matching
Organizer of the Session: ACMOS-51 or 645-367
How Did you learn about congress: Lai Khin Wee
IP ADDRESS: 137.248.56.27
