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A dual EM algorithm for TV regularized Gaussian mixture model in image segmentation

  • * Corresponding author: Jun Liu

    * Corresponding author: Jun Liu 
Abstract Full Text(HTML) Figure(12) / Table(3) Related Papers Cited by
  • A dual expectation-maximization (EM) algorithm for total variation (TV) regularized Gaussian mixture model (GMM) is proposed in this paper. The algorithm is built upon the EM algorithm with TV regularization (EM-TV) model which combines the statistical and variational methods together for image segmentation. Inspired by the projection algorithm proposed by Chambolle, we give a dual algorithm for the EM-TV model. The related dual problem is smooth and can be easily solved by a projection gradient method, which is stable and fast. Given the parameters of GMM, the proposed algorithm can be seen as a forward-backward splitting method which converges. This method can be easily extended to many other applications. Numerical results show that our algorithm can provide high quality segmentation results with fast computation speed. Compared with the well-known statistics based methods such as hidden Markov random field with EM method (HMRF-EM), the proposed algorithm has a better performance. The proposed method could also be applied to MRI segmentation such as SPM8 software and improve the segmentation results.

    Mathematics Subject Classification: Primary: 68U10.


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  • Figure 1.  Comparison of Dual EM-TV, Original EM-TV [26] and HMRF-EM methods for 2 classes segmentation

    Figure 2.  Enlarged red square regions in Figure 1

    Figure 3.  Comparison of Dual EM-TV, Original EM-TV [26] and HMRF-EM methods for 4 classes segmentation

    Figure 4.  Enlarged red square regions in Figure 3

    Figure 5.  An image with two regions, which are the same mean but different variance, and the histogram

    Figure 6.  Comparison of segmentation results of GMM-EM, Dual EM-TV and CV model on region with same mean but different variances image

    Figure 7.  Comparison between Dual EM-TV and HMRF-EM method

    Figure 8.  Segmentation results by Dual EM-TV in color images for two-region images

    Figure 9.  Segmentation results by Dual EM-TV in color images for multi-region images

    Figure 10.  Segmentation comparison under different noise levels. Noise standard deviation: A: 120/255, B: 240/255, C: 300/255, D: 390/255

    Figure 11.  Segmentation comparison of the same multi-region image by different noise levels. Noise standard deviation: A: 50/255, B: 60/255, C: 70/255, D: 80/255

    Figure 12.  Results of SPM8 with one slice of MR Images. First row: left image, MRI with 0% RF and 0% noise; right: MRI with 40% RF and 9% noise). Second and third rows: the first and third columns are the newsegment method for left and right images, respectively; the second and fourth columns are the proposed method for left and right images, respectively

    Table 1.  Comparison of 2 classes segmentation for Dual EM-TV, Original EM-TV [26] and HMRF-EM method (Heart image with size $ 615\times 615 $)

    Methods CPU time (seconds) Accuracy (SAI)
    Dual EM-TV 1.233 99.70%
    Original EM-TV [26] 9.482 98.02%
    HMRF-EM 4n 4.052 91.84%
    HMRF-EM 8n 3.052 97.38%
    %HMRF-EM 12n 2.653 98.69%
    HMRF-EM 20n 1.528 99.24%
     | Show Table
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    Table 2.  Comparison of 4 classes segmentation for Dual EM-TV, Original EM-TV [26] and HMRF-EM methods (4-color image with size $ 600\times 600 $)

    Methods CPU time (seconds) Accuracy (SAI)
    Dual EM-TV 1.699 99.90%
    Original EM-TV [26] 18.502 99.02%
    HMRF-EM 4n 3.480 97.39%
    HMRF-EM 8n 3.202 99.81%
    %HMRF-EM 12n 2.696 99.87%
    HMRF-EM 20n 2.388 99.83%
     | Show Table
    DownLoad: CSV

    Table 3.  Comparison of DM in SPM8 on brain images

    noise level 5% 5% 7% 7% 9% 9%
    brain part WM GM WM GM WM GM
    Dual EM-TV 0.9380 0.9122 0.9218 0.8970 0.9035 0.8818
    New Segment 0.9317 0.9099 0.9035 0.8822 0.8759 0.8536
     | Show Table
    DownLoad: CSV
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