尽管病理切片图像对配准的要求比较高，而现存的配准方法却难免会产生失配现象。为解决这一问题，提出了一种基于相关性的病理切片图像配准新算法。该算法是采用基于图片中两平行列(行)间的数据来抽取特征的方法。为了避免“干扰”的累积，该算法选择了差值曲线中最强的特征——最大包来作为模板的基元，并根据图片内容的相关性，利用最大包的分布作为模板来进行图像配准，从而降低了“干扰”的影响，使算法配准的稳定度比以前一些算法有较大的提高。实验证明，该算法不仅比较规整，稳定性高，而且计算速度也较快，是一种比较实用的图像配准算法。

The requirement of the images registration of pathologic slices is more demanding than others in quality, but some of the present methods of images registration will produce unmatched phenomena. In order to solve this problem, a new stitching algorithm of images based on relativity is presented here. In this algorithm, the character of the image is got from the two parallel columns (or lines) in the overlap area of images, which will be stitched up each other. To avoid the accumulation of the"disturbance", the maximal envelop is taken as the element of template ,which is the strongest character in the curve of difference data. And according to the relativity of the image content, the method of images registration will be proceeded by using the template which is made by the distribution of the maximal convex envelop, then the effect of the "disturbance" will be reduced, thus the method of images registration mentioned here is more steady than some former similar methods. Experiments demonstrate that this algorithm not only is more regular and stable, but also has a faster calculating speed. It's a more practical method of images registration.