Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation
Publish place: 19th Iranian conference on Biomedical Engineering
Publish Year: 1391
Type: Conference paper
Language: English
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Document National Code:
ICBME19_099
Index date: 29 January 2014
Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation abstract
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.
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Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation authors
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