Abbas Biniaz - Computational Neuroscience Laboratory, Sahand University of Technology
Ataollah Abbassi - Computational Neuroscience Laboratory, Sahand University of Technology
Mousa Shamsi - Department of Electrical Engineering, Sahand University of Technology
Afshin Ebrahimi - Department of Electrical Engineering, Sahand University of Technology

Conventional fu1.zy c-means (FCM) algorithm is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper aims to develop a Gaussian spatial FCM (gsFCM) for segmentation of brain magnetic resonance (MR) imag.es. The proposed algorithm uses fuzzy spatial information to update fuzzy membership with a Gaussian function. Proposed method has less sensitivity to noise specifically in tissue boundaries, angles, and borders than spatial FCM (sFCM). Furthermore by the proposed algorithm a pixel which is a distinct tissue from anatomically point of view for example a tumor in preliminary stages of its appearance, has more chance to be a unique cluster. The quantitative assessment of presented FCM techniques is evaluated by conventional validity functions. Experimental results show the efficiency of proposed algorithm in segmentation of MR images.

component; Segmenmtion; MRI; FCM; spatial information