Research On Image Segmentation Based On Active Contour Model

Image segmentation is a classic problem in the image processing area, but is still a hottopic. Among image segmentation methods, active contour model is widely studied in recentyears. The active contour model can be classified into two types: parametric active contourmodel and level set active contour model. The parametric active contour model sets aparametric curve with energy in the region of interest, and it minimizes the energy of thecurve to extract the objects. This paper has introduced three classical model, i.e., snake model,balloon snake model, and GVF snake model. Afterwards, the advantage and the disadvantageof the three models have been analyzed. However, the parametric active contour model can'thandle the topology change of the curve. To solve this problem, the level set active contourmodel is presented. In the level set framework, the curve is represented by the zero level set ofthe level set function. Moving the curves can be done by evolving the level set functionsinstead of directly moving the curves. The level set method can be categorized into twoclasses: edge-based model and region-based model. Edge-based models utilize image gradientto guide curve evolution, which are usually sensitive to noise, initial curve and weak edges.These methods can only detect objects with strong edges defined by the gradient.Region-based models tend to rely on region information to guide contour evolution. They areless sensitive to the initial curve, and have good performance in detecting the weak edge andthe interior and exterior boundaries. In this paper, we have introduced several region-basedmodels, i.e., C-V model, LBF model and LIF model, and compared their performance.In this paper, we have studied the existing problem in parametric active contour modeland level set active contour model, and proposed two improved methods:(1) As the balloon force snake model is sensitive to the initial contour and has theshortcoming of weak boundary leakage, an adaptive balloon force snake model based onfuzzy clustering is proposed, which integrates global information of the image into theballoon force snake model, and this model is used to image segmentation. The newlyproposed model can adaptively change the direction and magnitude of the balloon forceaccording to region information, which can make the contour converge to the target boundarycorrectly. The experimental results show that the improved model has lower restriction on thelocation of the initial curve, can solve the problem of weak boundary leakage and can make the curve converge on concave boundary correctly. Furthermore, the improved model hasstrong robustness to noise, and has no critical points problem of GVF.(2) As the LIF model is sensitive to the location of initial curve and is trapped into localminimums easily, an active contour model based on image region information is proposedwhich utilizes global and local image information to segment images, and its energyfunctional consists of four terms: local term, area term, length term and penalty term. Byutilizing the local image information, the proposed model can efficiently segment the imageswith intensity inhomogeneity. And introducing a global indicating function in the area termcan speed up the convergence of the proposed model and can also avoid being trapped intolocal minimums. In addition, the time-consuming re-initialization can be avoided because thepenalty term forces the level set function to be close a signed distance function. In order tosegment region of interest in the image, narrowband implementation of the proposed model ispresented. The experimental results demonstrate that the proposed model is less sensitive tothe location of initial curve, achieves more quick convergence, and can segment the imageswith intensity inhomogeneity accurately. Moreover, its narrowband implementation can onlyextract region of interest in the image.