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

Abstract

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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .