Research Of Background Removal Image Based On Region Scalable Fitting In Image Segmentation

Image segmentation is one of the most important research contents in the field of image processing and is widely used in real life. Such as remote sensing, biomedical image analysis, industrial automation, security, military and so on. Image segmentation methods based on PDE or calculus of variations play a huge role in promoting the development of image segmentation. But most of the involved models that are based on PDE or calculus of variations are non-convex, so they are easy to get into local minimums, and most of these experiment results which we get are not satisfactory. Besides, the calculation time of these models is too slow to meet the actual demand. Therefore, according to the background removal model and the regional fitting method, we proposed a new image segmentation model in this article. We divide this paper into following four parts:In the first part of this paper, we introduce the related content of image processing and some related theoretical knowledge about this paper.In the second part, we introduce the Split Bregman method.In the third part, following the principle of the background removal, we did some reforms to the original background removal model. With the application of region-scalable fitting method and Heaviside function we get a new region-scalable fitting background removal model. However, the improved model here is not a convex model, and cannot get the global minimum solution, so we make convex optimization to the improved model to get a convex model to solve this problem. Finally, by using the Split Bregman method and level set method, the global minimum solution of the model can be obtained.In the last part, we did some experiments and analyses. Comparing with ICV model, LK model and CV model, several numerical experiment results show that the proposed model in this article has a better performance on image segmentation. Meanwhile, the experiment also demonstrates that the proposed model in this article is more efficient than RSF model in the case of similar segmentation results. Finally the experiment results also show that different initial positions have little effect on image segmentation results which demonstrates that our model is low sensitive to initialize contour curve. When dealing with the MRI images and synthetic images, the model presents in this paper can not only obtain good segmentation results, but also has a high efficiency on segmentation. The experiment results also show that the model in this article is robust.