Ranked k-means clustering for terahertz image segmentation

It is known that k-means clustering is especially sensitive to initial starting centers. In this paper, we propose an original version of k-means for the segmentation of Terahertz images, called ranked-k-means, which is essentially less sensitive to the initialization of the centers. We present the ranked set sampling design and explain how to reformulate the k-means technique under the ranked sample to estimate the expected centers as well as the clustering of the observed data. Our clustering approach is tested on various Terahertz images. Experimental results show that k-means based on the ranked sample is more efficient than other clustering techniques.