Abstract
Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).
Highlights
Image segmentation is to divide the image into regions with different features
Intelligent algorithm can obtain global optimal solution quickly, and it is suitable for the complex data space of nonlinear multidimension while few approaches aim at combining artificial fish swarm algorithm (AFSA) with fuzzy c-means (FCM)
In addition to the visually qualitative results, we have introduced many numerical indexes to evaluate the accuracy of segmentation results
Summary
Image segmentation is to divide the image into regions with different features. As an important procedure in image processing, image segmentation is a hotspot and difficulty in medical image technology field [1]. FCM [4,5,6] is the mainstream algorithm in fuzzy clustering method It has advantages of unsupervised, simple implementation, no threshold set, and practicality, but at the same time it has the disadvantages of sensitivity to random initial value, falling into local optimal solution, and large calculation under the multidimensional space. Liu et al [20] proposed a dynamic fuzzy clustering method based on artificial fish swarm algorithm by introducing a fuzzy equivalence matrix. He et al [21] verified that AFSA with adaptive visual and step combining with FCM is more superior to genetic algorithm. These verify that HAFSA is effective and feasible in overcoming the sensitivity of initial value and noise
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