Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Breast cancer malignancy identification using self-organizing map
CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing
Automatic detection of fetal nasal bone in 2 dimensional ultrasound image using map matching
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Ultrasonic marker pattern recognition and measurement using artificial neural network
SIP'10 Proceedings of the 9th WSEAS international conference on Signal processing
Automated ultrasonic measurement of fetal nuchal translucency using dynamic programming
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Segmentation of prostate tumor for gamma image using region growing method
Proceedings of the 15th WSEAS international conference on Computers
Extended robust diffusion algorithm for two dimensional ultrasonic images
Proceedings of the 15th WSEAS international conference on Systems
Proceedings of the 15th WSEAS international conference on Systems
Two dimensional automated movement ultrasound breast scanning system
Proceedings of the 15th WSEAS international conference on Systems
Computational 3D ultrasound volumetric rendering; an object oriented open-sources approach
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
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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 consistent and more objective results.