Fuzzy C-means clustering with spatially weighted information for medical image segmentation

Image segmentation is an essential process in image analysis and is mainly used for automatic object recognition. Fuzzy c-means (FCM) is one of the most common methodologies used in clustering analysis for image segmentation. FCM clustering measures the common Euclidean distance between samples based on the assumption that each feature has equal importance. However, in most real-world problems, features are not considered equally important. To overcome this issue, we present a fuzzy c-means algorithm with spatially weighted information (FCM-SWI) that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. These weights are determined based on the distance between a corresponding pixel and the center pixel to indicate the importance of the memberships. Such a process leads to improved clustering performance. Experimental results show that the proposed FCM-SWI outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with spatial information, fast generation FCM) in both compactness and separation. Furthermore, the proposed FCM-SWI outperforms the classical algorithms in terms of quantitative comparison scores corresponding to a T1-weighted MR phantom for gray matter, white matter, and cerebrospinal fluid (CSF) slice regions.