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. 2016:2016:4516376.
doi: 10.1155/2016/4516376. Epub 2016 Jun 15.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

Zhang Yang  1 , Ye Shufan  2 , Guo Li  3 , Ding Weifeng  4
Affiliations

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

Zhang Yang et al. Biomed Res Int. 2016.

Abstract

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

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Figures

Figure 1
Figure 1
Original MRI images.
Figure 2
Figure 2
Segmentation results for the MRI1 and MRI2 brain images.
Figure 3
Figure 3
Segmentation results for MRI3 and MRI4.
See this image and copyright information in PMC

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