A novel feature extraction algorithm of ultrasound image combined with the texture, shape and the attenuation characteristics information was proposed, which could be used to identify the benign or malignant thyroid tumors. This paper focused on improving the texture feature extraction algorithm of thyroid tumors. On the basis of the traditional Local Binary Pattern (LBP) algorithm, we extended the neighborhood distribution in the elongated manner, which was more conducive to describe thyroid tumors and extract anisotropic properties of tumor effectively. Also, we used fuzzy logic to encode distance, which overcame the uncertainty of speckle noise in the ultrasound image. Furthermore, we extracted tumor circularity, standard deviation of the normalized radial length, area ratio, roughness index and the attenuation coefficient, which formed a feature vector to characterize thyroid tumors. Finally, Support Vector Machine (SVM) was used to classify and identify the thyroid nodules. Compared with other methods of feature extraction, the proposed feature fusion algorithm has a high accuracy of description, which can achieve higher classification accuracy, and the reasonableness and effectiveness of the proposed method is verified by experiments.