鉴于医学图像层间插值是医学数据可视化的一个关键环节，它直接影响人体组织器官的3维重建结果和医疗诊断的正确性和准确性，为此，针对传统医学图像层间插值方法精度不高和效率低下的缺陷，提出了一种基于像素分类的医学图像层间插值方法。该方法首先根据待插值图像与其相邻原始图像的对应像素的相关性对其像素点进行分类，然后采用不同的方式对不同类别的点进行插值，并对其进行错误校验。实验结果表明，该方法能够有效提高插值的效率和精度。

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.