An efficient clustering analysis method for image segmentation with noise

Abstract

One approach to image segmentation is to apply a data clustering method such as fuzzy c-means (FCM) to the pixels of the image. FCM and its variations all require an appropriately predefined number of clusters for a given set of data in order to obtain a correct clustering result However, an optimal number of clusters is usually unknown. Mok et al. proposed a robust adaptive clustering analysis method to identify the desired number of clusters and produce a reliable clustering solution at the same time based on a judgment matrix which represents the clustering relationship between any two data points. When applying the Mok's method to image segmentation, the method becomes very impractical because the judgment matrix is too huge to be handled efficiently. In this paper, a more efficient clustering analysis method is proposed for segmenting images with noise. The efficiency comes from the size of the judgment matrix which is only 256 by 256. Experimental results show that our method is better than Mok's method for segmenting both synthetic and real images with noise.