<P> In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-pixels). The goal of segmentation is to simplify and / or change the representation of an image into something that is more meaningful and easier to analyze . Image segmentation is typically used to locate objects and boundaries (lines, curves, etc .) in images . More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics . </P> <P> The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture . Adjacent regions are significantly different with respect to the same characteristic (s). When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching cubes . </P>

What is meant by segmentation in image processing