Abstract
In this paper, we propose a novel level set geodesic model for image segmentation. In our model, we define a hybrid signed pressure force (SPF) function integrating local and global region-based information to segment inhomogeneous images. The local region-based SPF utilizes mean values on local circular regions centered in each pixel. By introducing the local image information, the images with intensity inhomogeneity can be effectively segmented. In order to reduce the dependency on complex initialization, we incorporate a global region-based SPF into this model to develop a hybrid SPF. The global SPF and the local SPF are adaptively balanced by an adaptive weight. In addition, we also extend this model to four-phase level set formulation for brain MR image segmentation. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need for computationally expensive re-initialization. Experimental results indicate that the proposed method achieves superior segmentation performance in terms of accuracy and robustness.
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This study was supported by the National Natural Science Foundation of China (61472270 and 61402318).
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Zhang, L., Peng, X., Li, G. et al. A novel active contour model for image segmentation using local and global region-based information. Machine Vision and Applications 28, 75–89 (2017). https://doi.org/10.1007/s00138-016-0805-3
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DOI: https://doi.org/10.1007/s00138-016-0805-3