Gaussian kernel-based FCM segmentation of brain MRI with BPNN classification
by B. Thamaraichelvi; G. Yamuna
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 20, No. 2, 2016

Abstract: In this paper, the unsupervised Modified Gaussian Kernel-Based Fuzzy c-Means (MGKFCM) technique is proposed for the segmentation of magnetic resonance brain image and a Feed Forward Back Propagation Neural Network (FFBPNN) technique is presented for the classification of brain tissues. In this proposed MGKFCM algorithm, the Euclidean distance in the standard fuzzy c-means is replaced by a Gaussian radial basis function with additive bias field. The proposed method segments the given MRI data of the real image with the size of 443 × 708 × 3 automatically by considering the effects of intensity inhomogeneity, partial volume and noise. The performance of the proposed fuzzy technique has been found to be 98%. It has been compared with the existing magnetic resonance image segmentation techniques also. To separate the brain tissues into white matter (WM), gray matter (GM) and Cerebrospinal Fluid (CSF) from the fuzzy segmented output images, a different approach of FFBPNN classification technique has been used. The FFBPNN classifier has been trained already with the fuzzy segmented images. Sensitivity and specificity are the statistical measures used to analyse the performance of the proposed segmentation and selected classification tests.

Online publication date: Sat, 16-Jan-2016

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