Research Of Digital Image Segmentation

Image segmentation is a key image analysis technology, which the purpose is to pick out the regions or objectives of interest through analysis and study of the image. Image segmentation is an important step between image processing and image analysis, it is also the foundation of the further image understanding. Image segmentation has a long research history; it has been a hot research and focus, and thousands of algorithms were proposed. Although these methods have some extent and within a certain range solved some specific problems, cannot solve all the problems of image segmentation. Furthermore, there is not a general theory to evaluate the results of segmentation, so the research in this area faces many challenges.In the image segmentation, the details, edges and other information in images cannot be completely separated because of image noises, light, etc. So the image segmentation based on fuzzy technology can effectively segment the fuzzy degraded images.The paper studies the methods of digital image segmentation. The main works include:1. Analyzed the background of image segmentation, summarized the status of research and the development trend in domestic and foreign.2. Studied the pretreatment of the pre-segmentation, including compared the mean filter which can suppress Gaussian noise and the median filter which can suppress Salt & Pepper noise. On this basis, considering the types of noise which the images usually contain, proposed an improved PCNN (Pulse Coupled Neural Network) image filter algorithm. The method used different noise filters according to the different types of noise which each neuron was polluted.3. Research on threshold segmentation based on fuzzy theory. Analyzed the thresholding image segmentation in details, and after studying the fuzzy technology, proposed a new method which combined with fuzzy technology and Otsu thresholding segmentation. It can deal with the images which are fuzzy. The experiment results showed that it can improve the fuzzy, multi-objective, incomplete segmentation situation caused by noises, light and other interference factors.4. Improved the traditional Otsu, so it can overcome the thresholds trends to larger variance automatically, find the valley of histogram accurately. It can segment the small objective better. 5. Research on threshold segmentation based on genetic algorithm. To improve the segmentation efficiency, studied the image segmentation quickness. It used genetic algorithm which is one of the best optimization algorithms to optimize the multiple thresholds. The experiment results signified the method can find a set of optimal solutions accurately and the time-consuming is much less than the Simulated Annealing (SA) and the exhaustive method. At the same time, this method spent slightly more time than the Otsu and the maximum entropy method, but it can gain better segmentation effect. Compromise is the new segmentation with genetic algorithm can achieve better results.