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Denoising-based clustering algorithms for segmentation of low level salt-and-pepper noise-corrupted images

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2 Author(s)
Sulaiman, S.N. ; Imaging & Intell. Syst. Res. Team (ISRT), Univ. Sains Malaysia, Nibong Tebal, Malaysia ; Isa, N.A.M.

Clustering algorithm is a widely used segmentation method in image processing applications. The algorithm can be easily implemented; however in the occurrence of noise during image acquisition, this might affect the processing results. In order to overcome this drawback, this paper presents a new clustering-based segmentation technique that may be able to find different applications in image segmentation. The proposed algorithm called Denoising-based (DB) clustering algorithm has three variations namely, Denoising-based-K-means (DB-KM), Denoising-based-Fuzzy C-means (DB-FCM), and Denoising-based-Moving K-means (DB-MKM). The proposed DB-clustering algorithms are able to minimize the effects of the Salt-and-Pepper noise during the segmentation process without degrading the fine details of the images. These methods incorporate a noise detection stage to the clustering algorithm, producing an adaptive segmentation technique specifically for segmenting the noisy images. The results obtained quantitatively and qualitatively have favored the proposed DB-clustering algorithms, which consistently outperform the conventional clustering algorithms in segmenting the noisy images. Thus, these DB-clustering algorithms could be possibly used as pre- or post-processing (i.e., segmenting images into regions of interest) in consumer electronic products such as television and monitor with their capability of reducing noise effect.

Published in:

Consumer Electronics, IEEE Transactions on  (Volume:56 ,  Issue: 4 )

Date of Publication:

November 2010

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