Image segmentation is a common term for a variety of image operations. Image segmentation is a necessary step in any image processing task involving the labeling and identification of constituent parts of an image or scene. For example, it may be of interest to identify the number of items of a given color, size, or shape in an image. The simplest form of image segmentation splits the image into two parts, the object and background, based on the amplitude value of a pixel. This point-based method is described in Section 7.2. Similarly, it may be of interest to apply an image processing operator to a subregion with specific local characteristics. Presently, there are no general theories of segmentation. It is, however, well known that edges are important to mammalian vision. This is one of the reasons why edge detection in digital image processing has received a significant amount of attention. As a result, relatively effective linear and nonlinear techniques for the detection of edges in a grayscale or color image have been developed. These are described in Section 7.3 (for a morphological method of boundary detection see Section 6.5). In Section 7.4 we discuss amplitude features, local measures of amplitude variations that may be used for purposes of region-based segmentation. Section 7.5 presents image segmentation by clustering using the K-means algorithm [Har75, Did80]. Finally, in Section 7.6, a color-based segmentation example is presented.