In recent years, 3D image formats have become more and more popular, providing the possibility of examining texture as volumetric characteristics. This study extended traditional 2D Grey Level Co-occurrence Matrix (GLCM) to a 3D form. For 2D GLCM analysis, a primary issue was to determine the optimal window (kernel) sizes in the computational process. Previous studies demonstrated that the window size could account for 90% of the variability in the results of classification. Therefore, how to determine the most appropriate window size for GLCM computation has become a critical issue. In order to solve this problem, an extended semi-variance analysis was proposed to determine the optimal kernel size for 3D GLCM. Experimental results of this study indicated that the proposed extended semi-variance analysis could successfully identify appropriate kernel sizes for the 3D GLCM computation.