I. IMAGE SEGMENTION Image segmentation may be defined as the process of partitioning a digital image into several segments. These segments are sets of pixels, also known as the super pixels. The purpose of segmentation is to simplify an image or change its method of representation in a more meaningful and easier to analyze and interpret. This technique is used to locate objects and contours in an image or in a frame of a video sequence. A more precise definition would be: image segmentation is the process of assigning a label to each pixel in an image such that pixels with the same label have the same shared a visual feature [4]. The result of the segmentation of the image is a set of segments that together cover the entire image area. Also, a different result may be a set of contours extracted from the image. All of the pixels in the same region are similar with reference to a feature (color, intensity, texture). An important observation is that adjacent regions differ significantly in terms of the feature that brings together the pixels in a region. The segmentation operation has played a decisive role in the field of image processing, being in most of the cases a primary operation whose results have a crucial importance for the following image processing operations. This applies also when using image processing for evaluating the risk of the cutaneous lesions to turn into melanoma.