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MRI Segmentation using Fuzzy C-Means and Radial Basis Function Neural Networks


Affiliations
1 Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Iran, Islamic Republic of
2 Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran, Islamic Republic of
 

Image segmentation is one of the major preprocessing steps of magnetic resonance imaging (MRI) analysis in many medical and research applications. Accurate differentiation between three major soft tissues of the brain – grey matter, white matter and cerebrospinal fluid – is a key step in structural and functional brain analysis, visualization of the brain’s anatomical structures and measurement, diagnosis of neurodegenerative disorders and image-guided interventions as well as surgical planning. We propose a new methodological approach in segmentation of MRI images of the brain structure. Although various methods for MRI segmentation have been proposed, improvement of soft, automatic and precise MRI segmentation methods are worth a try. The proposed method has almost the same results as those from recent efforts in this field. However, it performs better in the presence of noise and RF-filed inhomogeneity.

Keywords

Fuzzy C-Means, Magnetic Resonance Imaging, Neural Networks, Segmentation, Radial Basis Function.
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  • MRI Segmentation using Fuzzy C-Means and Radial Basis Function Neural Networks

Abstract Views: 272  |  PDF Views: 70

Authors

A. H. Rasooli
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Iran, Islamic Republic of
M. Ashtiyani
Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran, Islamic Republic of
P. M. Birgani
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Iran, Islamic Republic of
S. Amiri
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Iran, Islamic Republic of
P. Mirmohammadi
Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran, Islamic Republic of
M. R. Deevband
Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran, Islamic Republic of

Abstract


Image segmentation is one of the major preprocessing steps of magnetic resonance imaging (MRI) analysis in many medical and research applications. Accurate differentiation between three major soft tissues of the brain – grey matter, white matter and cerebrospinal fluid – is a key step in structural and functional brain analysis, visualization of the brain’s anatomical structures and measurement, diagnosis of neurodegenerative disorders and image-guided interventions as well as surgical planning. We propose a new methodological approach in segmentation of MRI images of the brain structure. Although various methods for MRI segmentation have been proposed, improvement of soft, automatic and precise MRI segmentation methods are worth a try. The proposed method has almost the same results as those from recent efforts in this field. However, it performs better in the presence of noise and RF-filed inhomogeneity.

Keywords


Fuzzy C-Means, Magnetic Resonance Imaging, Neural Networks, Segmentation, Radial Basis Function.

References





DOI: https://doi.org/10.18520/cs%2Fv115%2Fi6%2F1091-1097