Brain MRI segmentation using the mixture of FCM and RBF neural network

One of the most commonly used methods for Magnetic Resonance Imaging (MRI) segmentation is Fuzzy C-Means (FCM). This method in comparison with other methods preserves more information of the images. Because of using the intensity of pixels as a key feature for clustering, Standard FCM is sensitive to noise. In this study in addition to intensity, mean of neighbourhood of pixels and largest singular value of neighbourhood of pixels are used as features. Also a method for segmenting MRI images is presented which uses both FCM and Radial Basis Function (RBF) neural network and partly decreases the limitation of standard FCM.

[1]  W. Peizhuang.  Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek) , 1983 .

[2]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[3]  Miao Qi,et al.  A Modified FCM Algorithm for MRI Brain Image Segmentation , 2008, 2008 International Seminar on Future BioMedical Information Engineering.

[4]  Ping Wang,et al.  A Modified FCM Algorithm for MRI Brain Image Segmentation , 2008 .

[5]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  M. A. Balafar.  Fuzzy C-mean based brain MRI segmentation algorithms , 2012, Artificial Intelligence Review.

[7]  Reza Ghaderi,et al.  Separation of brain tissues in MRI based on multi-dimensional FCM and spatial information , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[8]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[9]  Surendra Ranganath,et al.  Cloud basis function neural network: A modified RBF network architecture for holistic facial expression recognition , 2008, Pattern Recognit..

[10]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[11]  Sunil K Jha,et al.  Denoising by Singular Value Decomposition and Its Application to Electronic Nose Data Processing , 2011, IEEE Sensors Journal.

[12]  Meng Joo Er,et al.  Face recognition with radial basis function (RBF) neural networks , 2002, IEEE Trans. Neural Networks.

[13]  Mohammad Teshnehlab,et al.  Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation , 2010, Eng. Appl. Artif. Intell..

[14]  Ming-Feng Tsai,et al.  Accurate and Analytical Statistical Spatial Correlation Modeling Based on Singular Value Decomposition for VLSI DFM Applications , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.