Ultrasound Image Classification for Down Syndrome During First Trimester Using Haralick Features
نویسنده
چکیده
Keyword Down syndrome, Trisomy, Nuchal Translucency, Chromosomal Abnormalities, Gray Level Cooccurrence Matrix(GLCM), Support Vector Machine (SVM) Down syndrome or Trisomy 21 is a genetic disorder which causes mental disability to the baby during the gestation period. Ultrasound scan, a noninvasive test which includes ultrasound fetal image scan for the Nuchal Translucency measurement (NT). This work proposes a method to detect and classify the down syndrome images for Nuchal translucency from ultrasound scan images using Gray Level Co-occurrence Matrix (GLCM). Fourteen GLCM features are used for feature extraction. The features are classified using Support Vector Machine (SVM) classifier for normal NT and abnormal NT image. The high performance and classification rate of 94.4% is obtained using SVM classifier with Polynomial Kernel function.
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Image Classification for Ultrasound Fetal Images with Increased Nuchal Translucency during First Trimester Using SVM Classifier
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