Image Segmentation of LBF Model with Variable Coefficient Regularized Area Term
In this paper, an improved LBF model based on local regional information is proposed for image segmentation. The basic idea is to add the regularized area term to the energy function of the LBF model and establish a variable coefficient with adaptive capability composed of image regional information. Compared with the LBF model, the proposed model increases the driving force of the evolution curve, making the result better when dealing with the images with weak boundaries and intensity inhomogeneity. At the same time, it effectively solves the problem that the LBF model is sensitive to the initial position and size of the evolution curve. This model is used to segment medical images with complex topological structure and intensity inhomogeneity. Experimental results show that regardless of the initial curve of any position or any size, it has little influence on the segmentation result; moreover, the localization of deep-depressed image boundaries is more accurate, so we get the conclusion that the new model has corresponding improvements in segmentation accuracy and robustness.
KeywordsLBF model Medical image segmentation Intensity inhomogeneity Level-set method Model adaptability
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