부산대학교 도서관

Traditional fuzzy c-means (FCM) algorithm, a popular method in data clustering and image segmentation, is known to be sensitive to noise and cluster size, as it does not consider any spatial information and tends to balance cluster populations. FLICM (fuzzy local information c-means) and siibFCM (size-insensitive integrity-based FCM) are two of FCM variation algorithms, each demonstrated effective on overcoming noise or size sensitivity problem. This paper presents a variation of siibFCM, called noise- and size-insensitive integrity-based FCM and denoted as nsiibFCM. Similar to FLICM, nsiibFCM incorporates a local similarity measure based on intensity and purity of each neighboring pixel to remove noise while preserving details, where purity is defined as the normalized difference between the distance from the data point to the center of its assigned cluster and the distance from the data point to its nearest cluster center. Meanwhile, nsiibFCM uses a condition value calculated by using both size and integrity of a cluster, which are presented in siibFCM, to prevent centers of small clusters from drifting toward the adjacent larger and dispersive distributed clusters. Experimental results on both synthetic and real-world images show that our proposed nsiibFCM can effectively partition noisy images with balance- or unbalance-populated and dispersive-distributed clusters.