Thyroid Ultrasound Images Classification using the Shearlet Coefficients and the Generic Fourier Descriptor

To ameliorate the classification accuracy of the thyroid ultrasound imaging computer-aided diagnosis (CAD) system based on feature extraction, we used the Shearlet Transform (ST) to extract texture features, and the Generic Fourier Descriptor (GFD) to extract shape feature descriptor based on contours information. The ST supplies a rotation invariant descriptor at various scales. The GFD descriptor is autonomous, robust, and has no redundant features. Then, we applied a feature selection method on the extracted shearlet descriptor to build up the performance metrics. Finally, we used the objective metrics(sensitivity, specificity, and accuracy) to validate the performance of the proposed method. Experimentally, we apply our novel methods on a public dataset and we use the Support Vector Machine(SVM) and Random Forest (RF) as classifier. The obtained results prove the superiority of the proposed method.