WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 12, 2013
An Improved MRI Brain Image Segmentation to Detect Cerebrospinal Fluid Level Using Anisotropic Diffused Fuzzy C Means
Authors: , ,
Abstract: Cerebrospinal Fluid (CSF) is a clear colorless fluid produced in the brain. The changes in CSF protein levels form abnormal brain deposits strongly linked to variety of neurological diseases. Magnetic Resonance Imaging (MRI) of Brain is segmented using Fuzzy C Means (FCM) to detect the CSF level in brain. However, FCM is not suitable to segment the images with noise. This paper presents an algorithm known as Total Variation (TV) Regularization to solve the problems in FCM. Here TV is combined with FCM to eliminate noise but the method results in stair casing effect and takes longer reconstruction time. The proposed hybrid algorithm is the combination of Anisotropic Diffusion (AD) and TVFCM method, which overcomes the problems in traditional TVFCM. AD method first diffuses the image and then is convoluted using convolution filter and is then subjected to TVFCM segmentation. The performance of the proposed method finds the CSF level present in the MRI Brain images with 98% of accuracy, 92% of sensitivity and 97% of specificity. When compared to the traditional FCM and TVFCM, ADTVFCM yields better segmentation accuracy.
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Keywords: Cerebrospinal Fluid, Segmentation, Magnetic Resonance Image, Fuzzy C Means, Total Variation Regularizer, Anisotropic Diffusion