![]() ![]() Jung M., Peyré G., Cohen L.D., Non-local active contours, in: Lect.Ji Z., Xia Y., Sun Q., Cao G., Chen Q., Active contours driven by local likelihood image fitting energy for image segmentation, Inf.Jalba A.C., van der Laan W.J., Roerdink J.B.T.M., Fast sparse level-sets on graphics hardware, IEEE Trans.He C., Wang Y., Chen Q., Active contours driven by weighted region-scalable fitting energy based on local entropy, Signal Process.Estellers V., Zosso D., Lai R., Osher S., Thiran J.-.P., Bresson X., Efficient algorithm for level set method preserving distance function, IEEE Trans. ![]() Ding K., Xiao L., Weng G., Active contours driven by local pre-fitting energy for fast image segmentation, Pattern Recognit.Chan T.F., Vese L.A., Active contours without edges, IEEE Trans.Brox T., Cremers D., On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional, Int.Ben Ayed I., Mitiche A., A partition constrained minimization scheme for efficient multiphase level set image segmentation, in: Proc.Extensive experiments have demonstrated that the proposed method is superior to state-of-the-art active contour methods in terms of time efficiency and noise robustness. Furthermore, to decrease time costs, we use the sparse field method (SFM) and compute the means and variances of the intensities in each local region before the evolution of the contour. Additionally, the conditional probability of the image intensity in each local region is assumed to satisfy a Gaussian distribution with different means and deviations. The connectivity maps enhance noise robustness by building a relationship between a pixel and its adjacent pixels. Based on the framework of Bayes theorem, a spatial regularization of connectivity maps based on a Markov random field (MRF) is introduced as the prior probability in our model. To simultaneously strengthen the anti-noise ability and preserve the distinction in segmenting images with intensity inhomogeneity, we propose characterizing image regions using local prior region descriptors under the Bayesian criterion for image segmentation. However, this process can hardly segment images well when influenced by different noise. Local region-based active contour methods have been widely used to segment images with intensity inhomogeneity. ![]()
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