Laser & Optoelectronics Progress, Volume. 55, Issue 5, 051011(2018)

Medical Image Segmentation Model Based on Local Sparse Shape Representation

Hongbing Yao, Jinwen Bian*; , Jiawei Cong, and Yin Huang
Author Affiliations
  • School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
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    Figures & Tables(9)
    All prior shapes
    Original figure of bone. (a) Normalized original figure; (b) figure with Gaussian white noise and occlusion
    Segmentation process of proposed model. (a) Original contour; (b)-(e) evolution processing
    Partial prior shapes
    (a) Original figure of kidney; (b) sparse coefficient; (c) segmentation result
    Segmentation results. (a) Original image of the kidney; (b) Chan-Vese model; (c) ESC model; (d) proposed model
    Segmentation results. (a) Original image of the liver; (b) Chan-Vese model; (c) DG-SSR model; (d) proposed model
    • Table 1. Segmentation results of three methods for kidney

      View table

      Table 1. Segmentation results of three methods for kidney

      MethodSER
      Chan-Vese0.8240.423
      ESC0.8620.320
      Proposed method0.9430.122
    • Table 2. Segmentation results of three methods for liver

      View table

      Table 2. Segmentation results of three methods for liver

      MethodSER
      Chan-Vese--
      DG-SSR0.8870.127
      Proposed method0.9050.105
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    Hongbing Yao, Jinwen Bian, Jiawei Cong, Yin Huang. Medical Image Segmentation Model Based on Local Sparse Shape Representation[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051011

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    Paper Information

    Category: Image processing

    Received: Nov. 3, 2017

    Accepted: --

    Published Online: Sep. 11, 2018

    The Author Email: Jinwen Bian ( ronnie_bian@foxmail.com)

    DOI:10.3788/LOP55.051011

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