Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 256-258,277.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Double Level Set Algorithm Based on NL-Means Denosing Method for Brain MR Images Segmentation

TANG Wen-jie, ZHU Jia-ming XU Li   

  1. School of Information Engineering,Yangzhou University,Yangzhou,Jiangsu 225127,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: This paper proposed a novel double level set algorithm based on NL-Means denosing method for brain MR image segmentation,which has a large amount of noise and complicated background,and cannot be separated completely by traditional level set.First of all,this algorithm gets the denoised image by analyzing the image with NL-Means denosing method.Then,the algorithm identifies denoised image by segmenting the analyzed results in terms of improved double level set model.In order to deal with the effect of intensity inhomogeneities on the medical image,the algorithm introduces a bias fitting term into the improved double level set model and optimizes the denosing method result.The experimental result shows that the algorithm can reduce the problems of intensity inhomogeneities and noise,can separate brain MR image including intensity inhomogeneities and noise completely,and can obtain the expected effect of segmentation.

Key words: Medical image, NL-Means, Double level set, Bias correction

CLC Number: 

  • TP391
[1]AUJOL J F,CHAN T F.Combining geometrical and textured information to perform image classification[J].Journal of Visual Communication and Image Representation,2006,17(5):1004-1023.
[2]VESE L A,CHAN T F.A multiphase level set framework for image segmentation using the mumford and shah model [J].International Journal of Computer Vision,2002,50(3):271-293.
[3]詹天明,韦志辉,张建伟,等.脑MR图像分割和偏移场矫正的耦合水平集模型[J].中国图象图形学报,2011(11):2017-2023.
[4]唐文杰,朱家明,张辉.多分辨率双水平集医学图像分割算法[J].计算机科学,2017,44(S2):189-192.
[5]EFROS A A,LEUNG T K.Texture synthesis by non-parametric sampling[C]∥The Proceedings of the Seventh IEEE International Conference on Computer Vision.IEEE,1999,2:1033-1038.
[6]ZHENG Y H,WEN X Z,TIAN W.2DPCA based nonlocal means filter[C]∥2010 IEEE 10th International Conference on Signal Processing (ICSP).IEEE,2010:996-999.
[7]Boulanger J,Kervrann C,Bouthemy P.Adaptive spatio-temporal restoration for 4D fluorescence microscopic imaging[C]∥Medical Image Computing and Computer-Assisted Intervention(MICCAI 2005).2005:893-901.
[8]QIAN S,WENG G.Medical image segmentation based on FCM and Level Set algorithm[C]∥IEEE International Conference on Software Engineering and Service Science.IEEE,2017:225-228.
[9]WANG H,ZHUO Z,WU J,et al.Self-adaptive level set me-thods combined with geometric active contour[C]∥IEEE International Conference on Signal and Image Processing.IEEE,2017:578-581.
[10]LI B N,CHUI C K,CHANG S,et al.Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation[J].Computers in Biology and Medicine,2011,41(1):1-10.
[11]ZHANG X W,MA F C,HAO P F,et al.Mechanical behavior of pathological and normal red blood cells in microvascular flow based on modified level-set method[J].Science China(Physics,Mechanics & Astronomy),2016,59(1):72-80.
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