Scalespace play an important role in many computer vision tasks. Automatic scale selection is the foundation of multi-scale image analysis, but its performance is still very subjective and empirical. To automatically select the appropriate scale for a particular application, a scale selection model based on information theory was proposed in this paper. The proposed model utilizes the mutual information as a measuring criterion of similarity for the optimal scale selection in multi-scale analysis, with applications to the image denoising and segmentation. Firstly, the multi-scale image smoothing and denoising method based on the morphological operator was studied. This technique does not require the prior knowledge of the noise variance and can effectively eliminate the changes of illumination. Secondly, a clusteringbased unsupervised image segmentation algorithm was developed by recursively pruning the Huffman coding tree. The proposed clustering algorithm can preserve the maximum amount of information at a specific clustering number from the information-theoretical point of view. Finally, for the feasibility of the proposed algorithms, its theoretical properties were analyzed mathematically and its performance was tested through a series of experiments, which demonstrate that it yields the optimal scale for the developed image denoising and segmentation algorithms.