In the field of medical imaging, the traditional local binary patt (LBP) and its improved algorithms are often sensitive to noise. Traditional LBPs are solely based on the signal information from local differences, and the binary quantization method oversimplifies the local texture features while disregarding the imaging information from the concave-convex regions between the high-order pixels and the neighboring sampling points. Therefore, we propose an improved Derived Mean Complete Local Binary Patt (DM_CLBP) algorithm based on high-order derivatives. In the DM_CLBP method, the grey value of a single pixel is replaced by the mean grey value of the rectangular area block, and the difference between pixel values in the area is obtained using the second-order differentiation method. Based on the calculation concept of the complete local binary patt (CLBP) algorithm, the cascade signs and magnitudes of the two components are encoded and recombined in DM_CLBP using a uniform patt. The results from the experiments showed that the proposed DM_CLBP descriptors achieved a classification accuracy of 94.4％. Compared with LBP and other improved algorithms, the DM_CLBP algorithm presented in this study can effectively differentiate between lesion areas and normal areas in thyroid MR (magnetic resonance) images and shows the improved accuracy of area classification.