[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2018.23.12.035

Noisy Image Segmentation via Swarm-based Possibilistic C-means

Yu, Jeongmin (Dept. of Culture Heritage Industry, Korea National University of Culture Heritage)

Publication Information

Journal of the Korea Society of Computer and Information / v.23, no.12, 2018 , pp. 35-41 More about this Journal

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

Noisy Image Segmentation; Possibilistic c-means; Swarm-based Clustering; MR brain Image; Noise level;

Citations & Related Records

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