Research On Key Technology Of The Image Segmentation

Applications about image segmentation have been in many fields, so the image segmentation technology is applied both to check cancer cell and lacuna of exact instrument in microcosmic view and process the images about landform in macroscopical view. In aforementioned applications, most depend on Image Segmentation to a great extent. But many cases are complex, especially in medical image. Although the methods to process medical images are more than a hundred, more results are not satisfying. Because mankind's vision system is mysterious and highly automatic, the advancement of automatic image identification was limited. So there is important significance for improve the ability of computer vision and realizing mankind's vision system to more deeply research image segmentation technology by theory, application and evaluation in effect.This paper is on the effect of raising the issue of image segmentation, including the image of the pretreatment, image feature extraction and image segmentation methods. Research on the image of the texture feature extraction methods and use self-organizing feature map neural network SOFM in image segmentation. The images pretreatment is in the QPSO algorithm-based optimization of the two-dimensional design of the IIR digital filter algorithm to filter images, in order to eliminate the non-image information, renew the real and useful information, enhance the relevant information could be detected and the largest ,at least to streamline the data, thereby improve the feature extraction and image segmentation reliability.For image texture feature of the problem, using Gabor filter method, has improved the image texture accuracy and the final image segmentation is in a higher degree of accuracy. Useing Gabor filter methods, can be hoped that the texture image frequency channel space on the decomposition of factors, such as channel the energy of the image texture characteristics. This is because only a single-scale analysis of texture image classification difficult to obtain good performance, a multi-resolution analysis wavelet transform, they can overcome these difficulties. Gabor filter access to the use of space IF frequency channel the energy information, but also use a low frequency of the space reserved on the structure of statistics, last used SOFM neural network texture image classification. This paper presents self-organizing feature map neural network SOFM image segmentation algorithm, so that the image not only divided and separated a small amount of calculation is more accurate results. QPSO algorithm based on the two-dimensional IIR digital filter designed to optimize the algorithm, the algorithm can be based on groups of biodiversity information to guide the search algorithm process and enhanced the algorithm to set aside the local optimum capacity and improve the optimization problem solving results. The results confirmed that the algorithm improves the effectiveness in this paper.