Robust FCM clustering algorithm with combined spatial constraint and membership matrix local information for brain MRI segmentation

This paper presents a robust fuzzy clustering algorithm for the segmentation of brain tissues in magnetic resonance imaging (MRI). The proposed method incorporates context-aware spatial constraint and local information of the membership matrix into the fuzzy c-means (FCM) clustering algorithm. Based upon this approach, an FCM clustering algorithm with joint spatial constraint and membership matrix local information (FCMS-MLI) for brain MRI segmentation is presented, which is more robust against noise and other artifacts. The proposed spatial constraint considers both local spatial and gray-level information adaptively, and to the best of the authors' knowledge for the first time, the membership matrix local information (MLI) of fuzzy clustering is extracted to be utilized besides the spatial constraint. The proposed method solves two significant drawbacks of spatial constraint-based FCM approaches, which are ineffectiveness in preserving image details as well as confronting noise and intensity non-uniformity (INU) simultaneously. These problems are caused due to utilizing spatial constraints solely. The presented context-aware spatial constraint makes the method robust against a high level of noise while preserving image details. Furthermore, employing the MLI technique improves segmentation results in the presence of noise concurrently with INU. In contrast to spatial constraint-based methods, which just use local information in the image domain, the FCMS-MLI technique utilizes information in both image and coefficient domains. Hence, the proposed method benefits from two different sources of information. Finally, several types of images, including synthetic images, simulated and real brain MR images are utilized to make a comparison among the performances of popular FCMS types (i.e. FCM algorithms with spatial constraint), some new methods and the proposed algorithm. Experimental results prove efficiency and robustness of the FCMS-MLI algorithm confronting different levels of noise and INU. (C) 2019 Elsevier Ltd. All rights reserved.