A hybrid level set model for image segmentation.

PloS One
Weiqin ChenBin Pan

Abstract

Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.

References

Feb 6, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·T F Chan, L A Vese
Feb 16, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·C Xu, J L Prince
Jun 2, 2009·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Li WangChiu-Yen Kao
Apr 27, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Chunming LiJohn C Gore
Nov 25, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Xi YangJie Li
Mar 18, 2015·IEEE Transactions on Cybernetics·Kaihua ZhangDavid Zhang
Oct 29, 2017·Computers in Biology and Medicine·Weihang ZhangJunfeng Chen
May 29, 2018·Magnetic Resonance Imaging·Guopeng HuangWenbo Zhang
Sep 22, 2018·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Junjie BaiXiaodong Wu

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