Study On The Key Technologies Of Image Segmentation Based On Level Set Method

Image segmentation, as an important aspect in image processing, has been a hot but a formidable problem in an image project. For decades, researchers have been constantly seeking and exploring new ways to image partition, in order to the addressed problems more close to practical applications. Level set methods, as a type of novel image processing technology based on partial differential equation, have the advantages of free topology changes and easy integration of multi-cues(color, texture, shape, etc.), the kind of methods has been attracting much attentions from scholars in image segmentation field in recent years. However, problems facing to people in image segmentation are becoming increasingly complex with more extensive applications of image processing technologies and increasing users'demands.In the context, focusing on critical problems in image segmentation to be addressed including: poor accuracy of image segmentation especially in complex scene, local minima problems commonly existing in level set methods, slow speed of segmentation, in a complex scene(mainly as intensity inhomogeneity, spectral hetegeneity, multiple objects with complex topology relation), the thesis presents some subjects to be studied. The subjects are mainly as follows:1. To solve the problem caused by intensity inhomogeneity usually existing in images, this thesis develops a variational level set method for image segmentation. Based on the fact that features of intensity inhomogeneity keep relatively stable in a neighboring range of a given image, a neighbor offset field estimation method is developed. On the basis, a variational level set model for image segmentation has been constructed in order to alleviate disturbances from intensity inhomogenity, the coupled weak boundary, and noises. In addition, the thesis proposes a simple and effective downlink random optimization method so as to search an approximately global optimal solution. The purpose is to solve problems of local minimum occurring to traditional level set methods. 2. To solve the problem of misclassification existing in multiple level set segmentation, this thesis studies on a multiple level set method based on non-parameterization density estimation. Specifically, Parzen Window density estimation is firstly introduced to develop samples analysis and density modelling in complex scenes in order to build a basic non-parameterization multiple level set segmentation framework. Then, a Gabor filter bank is used in texture analysis to improve the classification performance. In addition, a neighbor similarity function is suggested and then a new class constraint energy item is constructed in order to further alleviate misclassification caused by spectral heterogeneity or noises, etc..3. To overcome the shortages of traditional pixel-based segmentation methods, a study on multiple level set method driven by Conditional Random Fields is developed. Conditional Random Fields theory has unique advantages of spatial context analysis using probability graph model and probability inference in image segmentation. Based on the advantages, the thesis combines the theory with a multiple level set method. Specifically, a strategy used to multi-class image partition based on competition between classes is proposed to create a probability model. Secondly, the proposed probability model is combined into level set framework through a mapping operation of discrete variables into continuous ones, in order to enhance the ability of resistance to noises, details interferences and to improve accuracy of image segmentation.4. In order to solve the problem of low-efficiency in solving the level set methods, a fast level set method for image segmentation is studies. Specifically, this thesis presents a novel fasten method of level set model for image segmentation through a correlation analysis between the amount of energy changes of each level set function and sample density in order to fasten curve evolution driven by a change of samples density either for two-phase level set, multi-phase level set segmentation model .