关键词: 磁共振图像/
距离正则化水平集/
二维集合经验模式分解/
固有模式函数
English Abstract
Distance regularized level set evolution in magnetic resonance image segmention based on bi-dimensional ensemble empirical mode decomposition
Fan Hong1,Wei Wen-Jin1,
Zhu Yan-Chun2
1.School of Computer Science, Shaanxi Normal University, Xi’an 710062, China;
2.Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Fund Project:Project supported by the Natural Science Foundation of Shaanxi Province, China (Grant No. 2014JM2-6115), the Science and Technology Research and Development Program of Shaanxi Province, China (Grant No. 2012K06-36), and the National Natural Science Foundation of China (Grant No. 41271518).Received Date:01 February 2016
Accepted Date:27 April 2016
Published Online:05 August 2016
Abstract:Original image is directly processed by the existing image segmentation algorithms, which is easily affected by noise. A bi-dimensional ensemble empirical mode decomposition (BEEMD) method is introduced to improve the accuracy of MR image segmentation by distance regularized level set (DRLSE) method. The BEEMD method is the extension of one-dimensional noise assisted data analysis from ensemble empirical mode decomposition (EEMD). The key points of BEEMD are as follows. four-neighborhood optimization is used to find extermum; three-spline interpolation is used to obtain the envelope; amplitude standard of added white noise is restricted; a certain time of integration is used to avoid modality aliasing problem. The main steps of the proposed method are as follows. Firstly, the MR image is decomposed into a number of two-dimensional intrinsic mode functions (BIMF) by BEEMD method; different weighting coefficients are endued to BIMF for image reconstruction to enhance the segmentation target. Secondly, part of BIMF components are added into edge indicator function of DRLSE to recover the blurring boundary caused by Gauss smooth operation. Then DRLSE is used to segment the reconstructed MR image. High accuracy and robustness of proposed algorithm are obtained in both simulations and clinical MR images. However, compared with DRLSE, the proposed method is complex and time consuming because using BEEMD for preprocessing the segmentation image.
Keywords: magnetic resonance image/
distance regularized level set evolution/
bi-dimensional ensemble empirical mode decomposition/
intrinsic mode functions
