Abstract: In order to distinguish the benign or malignant thyroid nodules more accurately, we propose the
texture feature extraction algorithm based on improved CLBP (Completed Local Binary Pattern) model and
GLCM (Gray Level Co-occurrence Matrix) model. Firstly, the local variance information is introduced into the
traditional CLBP model to make the CLBP operator describe the local texture features more precisely. Then, it
is combined with the global texture features described by the GLCM model. Finally, the shape features such as
aspect ratio, roundness and compactness are combined and input to the SVM (Support Vector Machine)
classifier. In order to further improve the recognition rate, the SVM parameter optimization algorithm based on
particle swarm optimization combining with grid searching is proposed. The experimental results show that the
feature extracted by the algorithm presented in this paper has higher recognition rate for classification and
recognition, and the proposed parameter optimization algorithm is more efficient than the traditional ones.

Key words: gray level co-occurrence matrix (GLCM) model,  thyroid nodules recognition, parameter optimization, support vector machine, completed local binary pattern ( CLBP ) model