Image interpolation of cross sections is one of the key steps of medical visualization. It affects directly the results of reconstructed tissues or organs

which plays an important role in medical treatment and diagnose. The traditional interpolation methods are imprecise or of high computational complexity. Aiming at such problems

an interpolation method based on pixel classification is presented. The method classifies pixels of the image to be interpolated by the relativity of corresponding pixels of its neighbor original images. Then the different methods are adopted to interpolate the different points. In addition

error checkout is introduced to check the mismatching points. Experimental results show that not only the complexity of the proposed approach is reduced

but also its quantitative error frequency is less than the conventional methods.