Study Based On The Variational Level Set Methods For Image Segmentation

Image segmentation, which is a basic part of image processing, isthe premise of image understanding and target recognition, and has beena hot and difficult problem in the field of image processing and computervision. Level set method for image segmentation has advantages overtopological changes in a natural way and can be implemented by fusingmore information. Thus, in recent years, many researchers have also donea great deal of effort to improve the performance of the imagesegmentation algorithms. But level set method is still staying in thedeveloping stage, and can't obtain the satisfactory results when theimages with intensity inhomogeneity or complex homogeneous objectswith multiple regions and topological changes are segmented. Thus, theinvestigation of its theory and application should be improved.In this paper, the variational level set methods have been deeplyinvestigated. Some efficient algorithms have been proposed, such as localkernel-driven active contour model, multiple level set method withmultiresolution, statistical approaches to automatic level set imagesegmentation with multiple regions, and multilayer level set method withmultiple regions. The main works can be summarized as follows:(1) To solve the problem caused by intensity inhomogeneity inmedical images, we proposed local kernel-driven active contour (LKAC)model. By incorporating local image information, the proposed modelcan efficiently segment the image with intensity inhomogeneity. The levelset function can maintain an approximate signed distance function byintroducing a penalizing energy into the regularization term. Compared with the LIF model and LBF model, the LKAC model can greatlyimprove the computational cost due to no need for the convolutionoperation during iterations. The experimental results show the LKACmodel has better performance and higher computational efficiency thanthe LIF model and LBF model. In addition, the proposed model is notsensitive to initial conditions.(2) To solve the problem of misclassification existing in multiplelevel set method, multi-resolution level set method with multiple regionsis proposed. The N regions in the image are segmented using N1curvesand each curve represents one region, which avoids generating theoverlapped segmentation regions. A multi-resolution level set schema isproposed to avoid the energy functional in a local minimum, alleviatemisclassification caused by noises in remote sensing images, and toreduce the computational cost. To ensure the smoothness of the level setfunction and eliminate the requirement of re-initialization, the distanceregularizing term is added to maintain an approximate signed distancefunction.(3) To solve the unknown number of segmented regions in multiplelevel set methods, we propose a multi-region level set method with aregion merging prior based on statistical approach (MRLSM-RMP-SA).By incorporating a region merging prior term into the energy functional,the term makes some level-set functions disappear during curve evolutionand can obtain the ideal number of segmented regions. A Bayesian theory,which is used to compute the intensity probability in the whole imagedomain, and the Gaussian kernel function, which estimates the priorprobability, make the algorithm efficient and simple. Compared withmany multiphase level set methods, the experiments show only the MRLSM-RMP-SA method can obtain the ideal number of segmentedregions and get better segmentation results.(4) By introducing a conception of image layer, a multilayer levelset method for multi-region image segmentation is proposed. Differentfrom usual multiple level set methods, the double level set method isemployed to segment the images in each image layer. The objects areextracted when a termination condition for each image layer is satisfied.Then, a foreground-filled technique is used to fill the object regions withan average of the intensities of outer regions. The process is over untilthere are no objects to segment. In the whole process of curve evolution,it does not need artificial interference, and has low complexity and fasterconvergence speed.