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. 2011 Oct 26:6:103.
doi: 10.1186/1746-1596-6-103.

Spatial based expectation maximizing (EM)

M A Balafar  1
Affiliations

Spatial based expectation maximizing (EM)

M A Balafar. Diagn Pathol. .

Abstract

Background: Expectation maximizing (EM) is one of the common approaches for image segmentation.

Methods: an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.

Results: the findings show that the proposed algorithm produces higher similarity index.

Conclusions: experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.

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Figures

Figure 1
Figure 1
clustering using user-interaction (a) A real brain volume, (b) its 4 clusters and (c) two sub clusters of Cluster 3.
Figure 2
Figure 2
The segmentation results of applying EM1 and NWEM on a slice of image with 9% Rician noise. (a) Noisy image, (b) Ground-truth, Segmentation results of (c) NWEM and (d) EM1.
Figure 3
Figure 3
The segmentation results of applying EM1 and NWEM on a slice of image with 7% Rician noise. (a) Noisy image, (b) Ground-truth, Segmentation results of (c) NWEM and (d) EM1.
Figure 4
Figure 4
The average similarity indices ρ, rfp and rfn for different noise levels.
Figure 5
Figure 5
The average similarity index ρ for different neighbourhood sizes on simulated volume with 9% noise.
Figure 6
Figure 6
Average times required to segment a slice using the proposed algorithm (EM1) and NWEM.
Figure 7
Figure 7
The average similarity indices ρ for different noise levels.
Figure 8
Figure 8
The average similarity indices ρ for EM-1 and FCM extensions in different noise level.
Figure 9
Figure 9
The similarity index of proposed algorithm when applied for real volume.
Figure 10
Figure 10
The average similarity index, rfp and rfn of proposed algorithm when applied on 20 real volumes.
Figure 11
Figure 11
The average similarity index of different algorithms when applied on 20 real volumes.
Figure 12
Figure 12
The similarity index of proposed algorithm and neighbourhood based FCM extensions when applied on 20 real volumes.
Figure 13
Figure 13
The similarity index of different algorithms when applied on 20 real volumes.
See this image and copyright information in PMC

References

    1. Chang PL, Teng WG. "Exploiting the self-organizing map for medical image segmentation". Twentieth IEEE International Symposium on Computer-Based Medical Systems. 2007. pp. 281–288.
    1. Jan J. Medical image processing, reconstruction, and restoration: concepts and methods: CRC. 2006.
    1. Tian D, Fan L. "A Brain MR Images Segmentation Method Based on SOM Neural Network". The 1st International Conference on Bioinformatics and Biomedical Engineering. 2007. pp. 686–689.
    1. Jiang Y, Meng J, Babyn P. "X-ray image segmentation using active contour model with global constraints". 2007. pp. 240–245.
    1. Balafar MA. "New spatial based MRI image de-noising algorithm". Artifitial Intelligence Review. 2011. pp. 1–11.

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