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Edge detection is an unsolved problem in that, so far, there is no general optimal solution. However, edge detection provides rich information about the scene being observed. This is particularly true in range images, where 3D information is explicit. Many researchers have been taking advantage of edge detection information to improve the segmentation of range images by integrating edge detection with other different segmentation techniques. This paper presents a methodology to perform edge detection in range images in order to provide a reliable and meaningful edge map, which helps to guide and improve range image segmentation by clustering techniques. The obtained edge map leads to three important improvements: (1) the definition of the ideal number of regions to initialize the clustering algorithm; (2) the selection of suitable initial cluster centers; and (3) the successful identification of distinct regions with similar features. Experimental results that substantiate the effectiveness of this work are presented.