【正文】 probably has a threshold segmentation method, boundary integral method, area extraction method, the segmentation method of bining the theory of specific tools, etc. The method based on region in some rules, directly to the image is divided into multiple regions. While by detecting method based on edge contains the edges of the different regions, to obtain about the regional boundary contour description, achieve the goal of image segmentation, and with the method of region and edge through the interaction of regional segmentation and edge detection, segmentation results are obtained. An introduction to the image segmentationImage segmentation, image segmentation) is to divide the image into different typical areas and extract interested in technology and process of goal. This feature can be pixel gray scale, color, texture, etc., a predefined target can be corresponding to a single region corresponding to multiple regions. Image segmentation is the key step in the image processing to image analysis, occupy the important position in image engineering. On the one hand, it is the foundation of target expression, has important influence on feature measurement. Because, on the other hand, image segmentation and target expression based on segmentation, feature extraction and the parameter measurement and convert the original image into more abstract and more pact form, making it possible to higher level of image analysis and understanding. Image segmentation is an important image processing technology, it not only receive widespread attention and research, also get a lot of application in practice. Including target outline, the threshold image segmentation, image distinguish or for poor, target detection, target recognition and target tracking technology. In big ways, image segmentation method can be roughly divided into the method based on region, the method based on edge, edge area and the method of bining, and on the basis of using multiresolution image processing theory of multiscale segmentation method. The method based on region in some rules, directly to the image is divided into multiple regions. While by detecting method based on edge contains the edges of the different regions, to obtain about the regional boundary contour description, achieve the goal of image segmentation, and with the method of region and edge through the interaction of regional segmentation and edge detection, segmentation results are obtained. Image segmentation method based on region mainly has the histogram threshold method, region growing method, the random field model based on image method, slack marking area segmentation method, etc. This article mainly discuss the region growing method based on region segmentation. Region growing image segmentation method is a kind of ancient, the earliest region growing image segmentation algorithm is put forward by Levine et al. The method generally there are two ways, one is first given to segment the target object in the image of a small area, or seeds in the seed regions based on the pixels around constantly to certain rules to join, to eventually bine on behalf of all the pixels of the object as the purpose of an area。 The other is a first image segmentation into a lot of the consistency of the strong, such as small area pixel gray value of the same area, then according to certain rules to small regional integration into large area, achieve the goal of image segmentation, a typical region growing method such as t. c. Pong put forward based on the surface of the region growing method (facet) model, region growing method often can cause excessive segmentation, is the inherent drawback of the image segmentation into too much area.The definition of image segmentation: image segmentation by collective concept gives a formal definition as follows:To set R represents the entire image, region segmentation can be viewed as R of R into N .The loophole that meet the following five conditions set (a region)(1) for all I and j, I indicates j, with Ri studying Rj indicates\(2) for I = 1, 2 , N, P (Ri) = TRUE。(3) to I indicates j, P (Ri∪Rj) = FALSE。(4) for I = 1, 2, N, Ri is connected area P (Ri) of all elements in the collection of Ri logical predicate, on behalf of the empty set. The above four conditions respectively called pleteness, independence, similarity, mutual exclusivity, connectivity. Image segmentation is mainly research methodsImage segmentation is an indispensable technology in image processing, since the 1970 s has been attached great importance to by the people, have been proposed so far thousands of various types of segmentation algorithms, but the problem is now proposed segmentation algorithm is mostly aimed at specific problems, not there is a general image segmentation algorithm is suitable for all images, so there exists in recent years, every year there are hundreds of related research report published by the phenomenon. However, has not been set rules, it gives the application of image segmentation technology bring a lot of problems. Therefore, the study of image segmentation are deep, is currently one of the hot topics in the study of image processing. In image processing, image segmentation has an indispensable position in the analysis, it plays the essential role, can be thought of as between the middle tier of lowlevel and highlevel processing. In recent years there are many new ideas, new methods, or improved algorithm. Below a brief overview of some classic traditional methods. Image segmentation is dividing the image into several specific and unique properties of regional extracts interested in technology and process of the target, these features can be extracted pixel gray, color, texture, etc of the target can be a single corresponding area, can also be a corresponding multiple regions.There are many kinds of classification, image segmentation method here will segmentation method summarized into four categorie