What is Active Contour Model

Image segmentation is an important image technique well known by its utility and complexity. To extract the useful information from images or groups of images, an inevitable step is to separate the objects from the background. Segmentation is just the right process and technique required for this task. Image segmentation is often described as the process that subdivides an image into its constituent parts and extracts those parts of interest (objects). It is one of the most critical tasks in automatic image analysis, which is at the middle layer of image engineering. Image engineering (which is composed of three layers from bottom to top: (1) image processing, (2) image analysis, and (3) image understanding) is a new discipline and a general framework for all image techniques (Zhang, forthcoming). The history of segmentation of digital images using computers can be traced back to 40 years ago. In 1965, an operator for detecting the edges between different parts of an image, Roberts operator (also called Roberts edge detector), was introduced and used for partition of image components (Roberts, 1965). Since then, the field of image segmentation has evolved very quickly and has undergone great change (Zhang, 2001a). In this article, after an introduction and explanation of the formal definition of image segmentation as well as three levels of research on image segmentation, the statistics for the number of developed algorithms in these years are provided; the scheme for classifying different segmentation algorithms is discussed; and a summary of existing survey papers for image segmentation is presented. All these discussions provide a general picture of research and development of image segmentation in the last 40 years.