Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Noisy Image Segmentation via Swarm-based Possibilistic C-means

  • Yu, Jeongmin (Dept. of Culture Heritage Industry, Korea National University of Culture Heritage)
  • Received : 2018.11.12
  • Accepted : 2018.12.10
  • Published : 2018.12.31
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Abstract

In this paper, we propose a swarm-based possibilistic c-means(PCM) algorithm in order to overcome the problems of PCM, which are sensitiveness of clustering performance due to initial cluster center's values and producing coincident or close clusters. To settle the former problem of PCM, we adopt a swam-based global optimization method which can be provided the optimal initial cluster centers. Furthermore, to settle the latter problem of PCM, we design an adaptive thresholding model based on the optimized cluster centers that yields preliminary clustered and un-clustered dataset. The preliminary clustered dataset plays a role of preventing coincident or close clusters and the un-clustered dataset is lastly clustered by PCM. From the experiment, the proposed method obtains a better performance than other PCM algorithms on a simulated magnetic resonance(MR) brain image dataset which is corrupted by various noises and bias-fields.

Keywords

CPTSCQ_2018_v23n12_35_f0001.png 이미지

Fig. 1. Flowchart of the proposed swam-based PCM

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Fig. 2. Clustering results with respect to γ in the T1-weighed 96th MR brain image which corrupted by 9% Gaussian noise and 40% bias-field.

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Fig. 3. Segmentation results: (a) Original image with 7%/40%(Noise/bias-field level), (b) Ground true of image, (c) Result of PCM with user-defined centers, (d) Result of [10], (e) Result of [16], (f) Result of the proposed method.

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Fig. 4. Segmentation results: (a) Original image with 9%/20%(Noise/bias-field level), (b) Ground true of image, (c) Result of PCM with user-defined centers, (d) Result of [10], (e) Result of [16], (f) Result of the proposed method.

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Fig. 5. Segmentation results: (a) Original image with 9%/40% (Noise/bias-field level), (b) Ground true of White Matter (WM), Grey Matter (GM), Cerebrospinal Fluid (CSF) and total region, (c) Result of PCM with user-defined centers, (d) Result of [10], (e) Result of [16], (f) Result of the proposed method.

Table 1. Quantitative evaluation results for various methods

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