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     Research Journal of Applied Sciences, Engineering and Technology


Image Classification for Ultrasound Fetal Images with Increased Nuchal Translucency during First Trimester Using SVM Classifier

1R. Sonia and 2V. Shanthi
1Sathyabama University
2Department of Computer Applications, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology  2015  2:113-121
http://dx.doi.org/10.19026/rjaset.9.1385  |  © The Author(s) 2015
Received: September ‎07, ‎2014  |  Accepted: September ‎20, ‎2014  |  Published: January 15, 2015

Abstract

Increased Nuchal Translucency is an indicator of increased risk for Down syndrome, which is identified by measuring Nuchal Translucency from ultrasound fetal images during 11 to 13+6 weeks of gestation. Increased NT is associated with chromosomal abnormalities. In this study an efficient classification system based on Discrete Wavelet Transform (DWT) is proposed to detect the normal and abnormal images with NT. Feature extraction is an essential pre-processing step for pattern recognition and machine learning problems. In order to classify the ultrasound image accurately, the texture features must be extracted effectively. In the proposed system, wavelet band signature, energy is used as features to classify the ultrasound image for the detection of Down syndrome using Support Vector Machine (SVM) classifier. The experimental results of pre diagnosed database with Discrete wavelet Transform and SVM classifier give best results for classification of Down Syndrome images with Normal NT and abnormal NT.

Keywords:

Chromosomal abnormalities, Discrete Wavelet Transformation (DWT), Down syndrome, Nuchal Translucency (NT), Support Vector Machine (SVM) classifier,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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