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A dissimilarity measure between adjacent pixels of an image is usually determined by the intensity values of these pixels and therefore does not depend on statistics computed over the whole image domain. In this paper, new dissimilarity measures exploiting image statistics are proposed. This is achieved by introducing the notion of dissimilarity function defined for every possible pair of intensity values. Necessary conditions for generating a valid dissimilarity function are provided and a series of functions integrating image statistics are presented. For example, the joint probability of adjacent pixel values leads to the notion of frequent connectivity while the notion of dependent connectivity relies on the local mutual information. The usefulness of the proposed approach is demonstrated by a series of experiments on satellite image data.