Vessel Segmentation Under Non-Uniform Illumination: A Level Set Approach
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    Abstract:

    In this paper, a new level set segementation model is proposed and is coupled with the geometric information, the edge information and the region information. The new level set segementation model is aimed at a vessel segmentation in a non-uniform image with weak object boundaries. First, a multiscaled filter with a Hessian matrix, which has a anisotropic character, is used to identify the direction of vessels. Second, the edge information is embed into a energy functional by a fast edge integral method with a laplacian zero crossing algorithm. A new level set segmentation model based on information of geometric structure, edge and region is constructed by this method. This new model can segment vessels exactly on grayscale uneven images. Compared to GAC CV segmentation model and other improved models based on CV model, the method in this paper has a better accuracy and robustness.

    Reference
    [1] Kimmel R, Bruckstein AM. Regularized Laplacian zero crossings as optimal edge integrators. Int’l Journal of Computer Vision,2003,53(3):225-243. [doi: 10.1023/A:1023030907417]
    [2] Chen B, Lai JH, Ma JH. A coupled active contour model and its application in image segmentation. Journal of Image and Graphics,2007,12(3):444-449 (in Chinese with English abstract).
    [3] Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: A level set approach. IEEE Trans. on Pattern Analysisand Machine Intelligence, 1995,17(2):158-175. [doi: 10.1109/34.368173]
    [4] Caselles V. Geometric models for active contours. In: Proc. of the Int’l Conf. on Image Processing. 1995. 9-12. [doi: 10.1109/ICIP.1995.537567]
    [5] Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Communicationson Pure and Applied Mathematics, 1989,42(5):577-685. [doi: 10.1002/cpa.3160420503]
    [6] Chan TF, Vese LA. Active contours without edges. IEEE Trans. on Image Processing, 2001,10(2):266-277. [doi: 10.1109/83.902291]
    [7] Tsai A, Yezzi AJr, Wells WIII, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A. Model-Based curve evolution techniquefor image segmentation. In: Proc. of the 2001 IEEE Conf. on Computer Society. 2001. I-463-I-468. [doi: 10.1109/CVPR.2001.990511]
    [8] Michailovich O, Rathi Y, Tannenbaum A. Image segmentation using active contours driven by the bhattacharyya gradient flow.IEEE Trans. on Image Processing, 2007,16(11):2787-2801. [doi: 10.1109/TIP.2007.908073]
    [9] Chunming L, Chiu-Yen K, Gore JC, Zhaohua D. Minimization of region-scalable fitting energy for image segmentation. IEEETrans. on Image Processing, 2008,17(10):1940-1949. [doi: 10.1109/TIP.2008.2002304]
    [10] Lankton S, Tannenbaum A. Localizing region-based active contours. IEEE Trans. on Image Processing, 2008,17(11):2029-2039.[doi: 10.1109/TIP.2008.2004611]
    [11] Sum KW, Cheung PYS. A novel active contour model using local and global statistics for vessel extraction. In: Proc. of the 28thIEEE Int’l Conf. on EMBS Annual. 2006. 3126-3129. [doi: 10.1109/IEMBS.2006.260817]
    [12] Sum KW, Cheung PYS. Vessel extraction under non-uniform illumination: A level set approach. IEEE Trans. on BiomedicalEngineering, 2008,55(1):358-360. [doi: 10.1109/TBME.2007.896587]
    [13] Wang XF, Huang DS, Xu H. An efficient local Chan-Vese model for image segmentation. Pattern Recognition, 2010,43(3):603-618. [doi: 10.1016/j.patcog.2009.08.002]
    [14] Koller TM, Gerig G, Szekely G, Dettwiler D. Multiscale detection of curvilinear structures in 2-D and 3-D image data. In: Proc. ofthe 5th Int’l Conf. on Computer Vision. 1995. 864-869. [doi: 10.1109/ICCV.1995.466846]
    [15] Frangi AF, Wiro JN, Koen LV, Max AV. Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Interventation, 1998,1496:130-137. [doi: 10.1007/BFb0056195]
    [16] Li Q, Sone SDK. Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans.Medical Physics, 2003,30(8):2040-2051. [doi: 10.1118/1.1581411]
    [17] Malladi R, Kimmel R, Adalsteinsson D, Sapiro G, Caselles V, Sethian JA. A geometric approach to segmentation and analysis of3D medical images. In: Proc. of the Workshop on Mathematical Methods in Biomedical Image Analysis. 1996. 244-252. [doi:10.1109/MMBIA.1996.534076]
    [18] Ennis DB, Kindlmann G. Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. MagneticResonance in Medicine, 2006,55:136-146. [doi: 10.1002/mrm.20741]
    [19] Zhang Q, Wan YY, Wang WQ, Ma JY, Qian JY, GE JB. Intravascular ultrasound image segmentation based on active contourmodel and Contourlet multiresolution analysis. Optics and Precision Engineering, 2008,16(11):2303-2311 (in Chinese with Englishabstract).
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薛维琴,周志勇,张涛,李莉华,郑健.灰度不均的弱边缘血管影像的水平集分割方法.软件学报,2012,23(9):2489-2499

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History
  • Received:May 27,2011
  • Revised:August 10,2011
  • Online: September 05,2012
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