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Chapter 1INTRODUCTION1.1 MotivationImage segmentation is the process of classifying each pixel of an image into a setof classes, where the number of classes is much smaller than the number of uniquepixel values. The goal of image segmentation is to separate features from eachother and from background, where features are items of interest in an image. Forexample, we might want to separate different tissue types in a brain image: greymatter, white matter, bone, blood, and so on.To illustrate this idea, a simple simulated image is shown in figure 1.1, alongwith a segmented version. In this simulation, it is visually clear that there arethree segments. We want the computer to be able to detect these segments automatically;in this case, the underlying segments are reconstructed perfectly usingthe algorithm described in chapter 5. The details of the analysis of this simulationcan be found in section 5.3.2.Segmentation can be accomplished manually by a human expert who simplylooks at an image, determines borders between regions, and classifies each region.This is perhaps the most reliable and accurate method of image segmentation, becausethe human visual system is immensely complex and well suited to the task.However, modern data acquisition methods create a huge amount of image datafor which manual analysis would be prohibitively expensive and time-consuming.