Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation
Özet
Conventional fuzzy 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) images. 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. © 2012 IEEE.
Anahtar kelimeler
Atıf için künye
Biniaz, A., Abbassi, A., Shamsi, M., & Ebrahımı, A. (2012). Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation. 2012 19th Iranian Conference of Biomedical Engineering, ICBME 2012, Tehran (ss. 154–158). https://doi.org/10.1109/icbme.2012.6519676
BibTeX
@inproceedings{biniaz2012-172523,
author = {Biniaz, Abbas and Abbassi, Ataollah and Shamsi, Mousa and Ebrahımı, Afshın},
title = {Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation},
booktitle = {2012 19th Iranian Conference of Biomedical Engineering, ICBME 2012},
address = {Tehran},
year = {2012},
pages = {154--158},
doi = {10.1109/icbme.2012.6519676},
url = {https://akademik.atauni.edu.tr/yayinlar/172523-fuzzy-c-means-clustering-based-on-gaussian-spatial-information-for-brain-mr}
}