Yunyun Yang
ABSTRACT
With the rapid development of medical imaging technology, the image segmentation has a special significance in medical applications. It's known that intensity inhomogeneity is one of the important features of magnetic resonance (MR) images, which presents a quite challenge in MRI segmentation. In this paper the authors apply the split Bregman method for minimization of the improved active contour model combining local and global information dynamically to segment brain MR images. The authors have proved this model can segment synthetic and real images with intensity inhomogeneity. Numerical results show the accuracy and efficiency of this model. Besides, this model is also robust to noise. That is exactly the reason why the authors apply this model to segment brain MR images. The authors present this model in a multi-phase formulation and use it to segment brain MR images with multiple regions adjacent to each other. Then the authors have tested this proposed model with many brain MR images. Finally, comparisons with other models and experimental results have demonstrated the efficiency and accuracy of this method.
- Li, C., Huang, R. and Ding, Z., "A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI," IEEE Trans. Image Process. 20(7), 2007--2016 (2011). Google Scholar
Digital Library
- Li, C., Chris, G., Li, W. and Gore, J. C., "A robust parametric method for bias field estimation and segmentation of MR images," CVPR 2009. IEEE 218--223 (2009).Google Scholar
- Wang, L., Li, C., Sun, Q., Xia, D. and Kao, C., "Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation," J. Comput. Med. Imaging Graphics 33(7), 520--531 (2009).Google ScholarCross Ref
- Li, C., Huang, R. and Ding, Z., "A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity," Medical Image Computing and Computer-Assisted Intervention-MICCAI 2008. Springer Berlin Heidelberg 1083--1091 (2008). Google ScholarDigital Library
- Chan, T. F. and Vese, L. A., "Active contours without edges," IEEE Trans. Image Process. 10(2), 266--277 (2001). Google ScholarDigital Library
- Li, C., Kao, C., Gore, J. C. and Ding, Z., "Minimization of region-scalable fitting energy for image segmentation," IEEE Trans. Image Process. 17(10), 1940--1949 (2008). Google ScholarDigital Library
- Li, C. and Xu, C., "Distance regularized level set evolution and its application to image segmentation," IEEE Trans. Image Process. 19(12), 3243--3254 (2012). Google ScholarDigital Library
- Yang, Y. and Wu, B., "Split Bregman method for minimization of improved active contour model combining local and global information dynamically," Journal of Mathematical Analysis and Applications 389(1), 351--366 (2012).Google ScholarCross Ref
- Goldstein, T. and Osher, S., "The split Bregman method for L1 regularized problems," SIAM J. Imaging Sci. 2(2), 323--343 (2009). Google ScholarDigital Library
- Yin, W., Osher, S., Goldfarb, D. and Darbon, J., "Bregman iterative algorithms for L1-minimization with applications to compressed sensing," SIAM J. Imaging Sci. 1(1), 143--168 (2008). Google ScholarDigital Library
- Yang, Y., Li, C., Kao, C. and Osher, S., "Split Bregman method for minimization of region-scalable fitting energy for image segmentation," in Proceedings of the 6th International Symposium on Visual Computing, vol. part II, LNCS 6454, 117--128, Springer, (2010). Google ScholarDigital Library
- Yang, Y. and Wu, B., "Fast multiphase image segmentation model for images with inhomogeneity," Journal of Electronic Imaging 21(1) 013015 (2012).Google ScholarCross Ref
Index Terms
- Active contour model combining local and global information dynamically with application to segment brain MR images
Recommendations
Improved active contour model for multi-phase MR image segmentation and bias field correction
ICCSP '19: Proceedings of the 3rd International Conference on Cryptography, Security and PrivacyMagnetic resonance imaging (MRI) has been applied in the imaging of all tissues of human body. It is an important method for doctors to analyze the cause of diseases. In particular, the accurate segmentation of MR images is significant for doctors to ...
A novel active contour model for image segmentation using local and global region-based information
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 ...
A global and local active contour model based on dual algorithm for image segmentation
Most of local region-based active contour models in terms of the level set approach are able to segment images with intensity inhomogeneity. However, these models do not utilize global statistical information and are quite sensitive to the initial ...
Comments